development and application of hypothesis

2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

Share This Book

• Increase Font Size

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Best Gallup Access Alternatives & Competitors in 2024

Sep 6, 2024

Experimental vs Observational Studies: Differences & Examples

Sep 5, 2024

Interactive Forms: Key Features, Benefits, Uses + Design Tips

Sep 4, 2024

Closed-Loop Management: The Key to Customer Centricity

Sep 3, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

• Privacy Policy

Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Survey Instruments – List and Their Uses

Research Report – Example, Writing Guide and...

Significance of the Study – Examples and Writing...

Context of the Study – Writing Guide and Examples

Research Process – Steps, Examples and Tips

Data Interpretation – Process, Methods and...

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

How to Write a Dissertation: A Beginner’s Guide 

Back to School – Lock-in All Access Pack for a Year at the Best Price

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Sweepstakes
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Research and Hypothesis Testing: Moving from Theory to Experiment

• First Online: 14 November 2019

Cite this chapter

• Mark W. Scerbo 6 ,
• Aaron W. Calhoun 7 &
• Joshua Hui 8  

2617 Accesses

In this chapter, we discuss the theoretical foundation for research and why theory is important for conducting experiments. We begin with a brief discussion of theory and its role in research. Next, we address the relationship between theory and hypotheses and distinguish between research questions and hypotheses. We then discuss theoretical constructs and how operational definitions make the constructs measurable. Next, we address the experiment and its role in establishing a plan to test the hypothesis. Finally, we offer an example from the literature of an experiment grounded in theory, the hypothesis that was tested, and the conclusions the authors were able to draw based on the hypothesis. We conclude by emphasizing that theory development and refinement does not result from a single experiment, but instead requires a process of research that takes time and commitment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Research Questions and Research Design

An Introduction to Experimental Design Research

Looking Back

Badia P, Runyon RP. Fundamentals of behavioral research. Reading: Addison-Wesley; 1982.

Google Scholar  

Roeckelein JE. Elsevier’s dictionary of psychological theories. Amsterdam: Elsevier Science; 2006.

Maxwell SE, Delaney HD. Designing experiments and analyzing data: a model comparison perspective. 2nd ed. Mahwah: Erlbaum; 2004.

Graziano AM, Raulin ML. Research methods: a process of inquiry. 4th ed. Boston: Allyn and Bacon; 2000.

Passer MW. Research methods: concepts and connections. New York: Worth; 2014.

Weinger MB, Herndon OW, Paulus MP, Gaba D, Zornow MH, Dallen LD. An objective methodology for task analysis and workload assessment in anesthesia providers. Anesthesiology. 1994;80:77–92.

Article   CAS   Google Scholar  

Warm JS. An introduction to vigilance. In: Warm JS, editor. Sustained attention in human performance. Chichester: Wiley; 1984. p. 1–14.

Freud S. The standard edition of the complete psychological works of Sigmund Freud. Volume XIX (1923–1926) The ego and the id and other works. Strachey, James, Freud, Anna, 1895–1982, Rothgeb, Carrie Lee, 1925-, Richards, Angela, Scientific literature corporation. London: Hogarth Press; 1978.

Mill JS. A system of logic, vol. 1. Honolulu: University Press of the Pacific; 2002. p. 1843.

Ericsson KA, Krampe RT, Tesch-Romer C. The role of deliberate practice in the acquisition of expert performance. Psychol Rev. 1993;100:363–406.

Article   Google Scholar  

Darian S. Understanding the language of science. Austin: University of Texas Press; 2003.

Lazarus RS, Folkman S. Stress, appraisal, and coping. New York: Springer Publishing Company; 1984.

Calhoun AW, Gaba DM. Live or let die: new developments in the ongoing debate over mannequin death. Simul Healthc. 2017;12:279–81.

Goldberg A, et al. Exposure to simulated mortality affects resident performance during assessment scenarios. Simul Healthc. 2017;12:282–8.

Robinson KA, Saldanha IJ, Mckoy NA. Frameworks for determining research gaps during systematic reviews. Report No.: 11-EHC043-EF. Rockville: Agency for Healthcare Research and Quality (US); 2011.

O’Sullivan D, Wilk S, Michalowski W, Farion K. Using PICO to align medical evidence with MDs decision making models. Stud Health Technol Inform. 2013;192:1057.

PubMed   Google Scholar  

Christensen LB, Johnson RB, Turner LA. Research methods: design and analysis. 12th ed. Boston: Pearson; 2014.

Keppel G. Design and analysis: a researcher’s handbook. 2nd ed. Englewood Cliffs: Prentice-Hall; 1982.

Spielberger CD, Sydeman SJ. State-trait anxiety inventory and state-trait anger expression inventory. In: Maruish ME, editor. The use of psychological testing for treatment planning and outcome assessment. Hillsdale: Lawrence Erlbaum Associates; 1994. p. 292–321.

Cheng A, et al. Reporting guidelines for health care simulation research: extensions to the CONSORT and STROBE statements. Simul Healthc. 2016;11(4):238–48.

Turner TR, Scerbo MW, Gliva-McConvey G, Wallace AM. Standardized patient encounters: periodic versus postencounter evaluation of nontechnical clinical performance. Simul Healthc. 2016;11:174–2.

Baddeley AD, Hitch GJ. Working memory. In: Bower GH, editor. The psychology of learning and motivation: advances in research and theory. 8th ed. New York: Academic; 1974. p. 47–89.

Kuhn TS. The structure of scientific revolutions. Chicago: University of Chicago Press; 1962.

Newton-Smith WH. The rationality of science. London: Routledge & Keegan Paul; 1981.

Book   Google Scholar  

Download references

Author information

Authors and affiliations.

Department of Psychology, Old Dominion University, Norfolk, VA, USA

Mark W. Scerbo

Department of Pediatrics, University of Louisville School of Medicine, Louisville, KY, USA

Aaron W. Calhoun

Emergency Medicine, Kaiser Permanente, Los Angeles Medical Center, Los Angeles, CA, USA

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Mark W. Scerbo .

Editor information

Editors and affiliations.

Monash Institute for Health and Clinical Education, Monash University, Clayton, VIC, Australia

Debra Nestel

Department of Surgery, University of Maryland, Baltimore, Baltimore, MD, USA

Kevin Kunkler

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Scerbo, M.W., Calhoun, A.W., Hui, J. (2019). Research and Hypothesis Testing: Moving from Theory to Experiment. In: Nestel, D., Hui, J., Kunkler, K., Scerbo, M., Calhoun, A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_22

Download citation

DOI : https://doi.org/10.1007/978-3-030-26837-4_22

Published : 14 November 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-26836-7

Online ISBN : 978-3-030-26837-4

eBook Packages : Biomedical and Life Sciences Biomedical and Life Sciences (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Society Homepage About Public Health Policy Contact

Data-driven hypothesis generation in clinical research: what we learned from a human subject study, article sidebar.

Submit your own article

Register as an author to reserve your spot in the next issue of the Medical Research Archives.

Join the Society

The European Society of Medicine is more than a professional association. We are a community. Our members work in countries across the globe, yet are united by a common goal: to promote health and health equity, around the world.

Join Europe’s leading medical society and discover the many advantages of membership, including free article publication.

Main Article Content

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS—a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

Article Details

The  Medical Research Archives  grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the  Medical Research Archives .

Chapter 6 - Hypothesis Development

Chapter 6 overview.

This chapter discusses the third step of the SGAM, highlighted below in gold, Hypothesis Development.

A hypothesis is often defined as an educated guess because it is informed by what you already know about a topic. This step in the process is to identify all hypotheses that merit detailed examination, keeping in mind that there is a distinction between the hypothesis generation and hypothesis evaluation .

If the analysis does not begin with the correct hypothesis, it is unlikely to get the correct answer. Psychological research into how people go about generating hypotheses shows that people are actually rather poor at thinking of all the possibilities. Therefore, at the hypothesis generation stage, it is wise to bring together a group of analysts with different backgrounds and perspectives for a brainstorming session. Brainstorming in a group stimulates the imagination and usually brings out possibilities that individual members of the group had not thought of. Experience shows that initial discussion in the group elicits every possibility, no matter how remote, before judging likelihood or feasibility. Only when all the possibilities are on the table, is the focus on judging them and selecting the hypotheses to be examined in greater detail in subsequent analysis.

When screening out the seemingly improbable hypotheses, it is necessary to distinguish hypotheses that appear to be disproved (i.e., improbable) from those that are simply unproven. For an unproven hypothesis, there is no evidence that it is correct. For a disproved hypothesis, there is positive evidence that it is wrong. Early rejection of unproven, but not disproved, hypotheses biases the analysis, because one does not then look for the evidence that might support them. Unproven hypotheses should be kept alive until they can be disproved. One example of a hypothesis that often falls into this unproven but not disproved category is the hypothesis that an opponent is trying to deceive us. You may reject the possibility of denial and deception because you see no evidence of it, but rejection is not justified under these circumstances. If deception is planned well and properly implemented, one should not expect to find evidence of it readily at hand. The possibility should not be rejected until it is disproved, or, at least, until after a systematic search for evidence has been made, and none has been found.

There is no "correct" number of hypotheses to be considered. The number depends upon the nature of the analytical problem and how advanced you are in the analysis of it. As a general rule, the greater your level of uncertainty, or the greater the impact of your conclusion, the more alternatives you may wish to consider. More than seven hypotheses may be unmanageable; if there are this many alternatives, it may be advisable to group several of them together for your initial cut at the analysis.

Developing Multiple Hypotheses

Developing good hypotheses requires divergent thinking to ensure that all hypotheses are considered. It also requires convergent thinking to ensure that redundant and irrational hypotheses are eliminated. A hypothesis is stated as an "if … then" statement. There are two important qualities about a hypothesis expressed as an "if … then" statement. These are:

• Is the hypothesis testable; in other words, could evidence be found to test the validity of the statement?
• Is the hypothesis falsifiable; in other words, could evidence reveal that such an idea is not true?

Hypothesis development is ultimately experience-based. In this experienced-based reasoning, new knowledge is compared to previous knowledge. New knowledge is added to this internal knowledge base. Before long, an analyst has developed an internal set of spatial rules. These rules are then used to develop possible hypotheses.

Looking Forward

Developing hypotheses and evidence is the beginning of the sensemaking and Analysis of Competing Hypotheses (ACH) process. ACH is a general purpose intelligence analysis methodology developed by Richards Heuer while he was an analyst at the Central Intelligence Agency (CIA). ACH draws on the scientific method, cognitive psychology, and decision analysis. ACH became widely available when the CIA published Heuer’s The Psychology of Intelligence Analysis . The ACH methodology can help the geospatial analyst overcome cognitive biases common to analysis in national security, law enforcement, and competitive intelligence. ACH forces analysts to disprove hypotheses rather than jump to conclusions and permit biases and mindsets to determine the outcome. ACH is a very logical step-by-step process that has been incorporated into our Structured Geospatial Analytical Method. A complete discussion of ACH is found in Chapter 8 of Heuer’s book.

General Approaches to Problem Solving Utilizing Hypotheses

Science follows at least three general methods of problem solving using hypotheses. These can be called the:

• method of the ruling theory
• method of the working hypothesis
• method of multiple working hypotheses

The first two are the most popular but they can lead to overlooking relevant perspectives, data, and encourage biases. It has been suggested that multiple hypotheses offers a more effective way of overcoming this problem.

Ruling Theories and Working Hypotheses

Our desire to reach an explanation commonly leads us to a tentative interpretation that is based on a single case. The explanation can blind us to other possibilities that we ignored at first glance. This premature explanation can become a ruling theory, and our research becomes focused on proving that ruling theory. The result is a bias to evidence that disproves the ruling theory or supports an alternate explanation. Only if the original hypothesis was by chance correct does our analysis lead to any meaningful intelligence work. The working hypothesis is supposed to be a hypothesis to be tested, not in order to prove the hypothesis, but as a stimulus for study and fact-finding. Nonetheless, the single working hypothesis can become a ruling theory, and the desire to prove the working hypothesis, despite evidence to the contrary, can become as strong as the desire to prove the ruling theory.

Multiple Hypotheses

The method of multiple working hypotheses involves the development, prior to our search for evidence, of several hypotheses that might explain what are attempting to explain. Many of these hypotheses should be contradictory, so that many will prove to be improbable. However, the development of multiple hypotheses prior to the intelligence analysis lets us avoid the trap of the ruling hypothesis and thus makes it more likely that our intelligence work will lead to meaningful results. We open-mindedly envision all the possible explanations of the events, including the possibility that none of the hypotheses are plausible and the possibility that more research and hypothesis development is needed. The method of multiple working hypotheses has several other beneficial effects on intelligence analysis. Human actions are often the result of several factors, not just one, and multiple hypotheses make it more likely that we will see the interaction of the several factors. The beginning with multiple hypotheses also promotes much greater thoroughness than analysis directed toward one hypothesis, leading to analytic lines that we might otherwise overlook, and thus to evidence and insights that might never have been considered. Thirdly, the method makes us much more likely to see the imperfections in our understanding and thus to avoid the pitfall of accepting weak or flawed evidence for one hypothesis when another provides a more possible explanation.

Drawbacks of Multiple Hypotheses

Multiple hypotheses have drawbacks. One is that it is difficult to express multiple hypotheses simultaneously, and therefore there is a natural tendency to favor one. Another problem is developing a large number of hypotheses that can be tested. A third possible problem is that of the indecision that arises as an analyst balances the evidence for various hypotheses, which is likely preferable to the premature rush to a false conclusion.

Actions That Help the Analyst Develop Hypotheses

Action 1: Brainstorming . Begin with a brainstorming session with your knowledge team to identify a set of alternative hypotheses. Focus on the hypotheses that are:

• logically consistent with the theories and data uncovered in your grounding;
• address the quality and relationships of spaces.

State the hypotheses stated in an "if ... then" format, for example:

• If the DC Shooter is a terrorist, then the geospatial pattern of events would be similar to other terrorist acts.
• If the DC Shooter is a serial killer, then the geospatial pattern of events would be similar to other serial killers.

Action 2: Review the hypotheses for testability , i.e., can evidence be could found to test the validity of the statement.

Action 3: Check the hypotheses for falsifiability , i.e., could evidence reveal that such an idea is not true.

Action 4: Combine redundant hypotheses.

Action 5:Consider the elimination of improbable and unproven hypotheses.

• Hypothesis Testing: Definition, Uses, Limitations + Examples

Hypothesis testing is as old as the scientific method and is at the heart of the research process. 

Research exists to validate or disprove assumptions about various phenomena. The process of validation involves testing and it is in this context that we will explore hypothesis testing. 

What is a Hypothesis? 

A hypothesis is a calculated prediction or assumption about a population parameter based on limited evidence. The whole idea behind hypothesis formulation is testing—this means the researcher subjects his or her calculated assumption to a series of evaluations to know whether they are true or false. 

Typically, every research starts with a hypothesis—the investigator makes a claim and experiments to prove that this claim is true or false . For instance, if you predict that students who drink milk before class perform better than those who don’t, then this becomes a hypothesis that can be confirmed or refuted using an experiment.  

Read: What is Empirical Research Study? [Examples & Method]

What are the Types of Hypotheses? 

1. simple hypothesis.

Also known as a basic hypothesis, a simple hypothesis suggests that an independent variable is responsible for a corresponding dependent variable. In other words, an occurrence of the independent variable inevitably leads to an occurrence of the dependent variable. 

Typically, simple hypotheses are considered as generally true, and they establish a causal relationship between two variables. 

Examples of Simple Hypothesis  

• Drinking soda and other sugary drinks can cause obesity. 
• Smoking cigarettes daily leads to lung cancer.

2. Complex Hypothesis

A complex hypothesis is also known as a modal. It accounts for the causal relationship between two independent variables and the resulting dependent variables. This means that the combination of the independent variables leads to the occurrence of the dependent variables . 

Examples of Complex Hypotheses  

• Adults who do not smoke and drink are less likely to develop liver-related conditions.
• Global warming causes icebergs to melt which in turn causes major changes in weather patterns.

3. Null Hypothesis

As the name suggests, a null hypothesis is formed when a researcher suspects that there’s no relationship between the variables in an observation. In this case, the purpose of the research is to approve or disapprove this assumption. 

Examples of Null Hypothesis

• This is no significant change in a student’s performance if they drink coffee or tea before classes. 
• There’s no significant change in the growth of a plant if one uses distilled water only or vitamin-rich water. 

Read: Research Report: Definition, Types + [Writing Guide]

4. Alternative Hypothesis 

To disapprove a null hypothesis, the researcher has to come up with an opposite assumption—this assumption is known as the alternative hypothesis. This means if the null hypothesis says that A is false, the alternative hypothesis assumes that A is true. 

An alternative hypothesis can be directional or non-directional depending on the direction of the difference. A directional alternative hypothesis specifies the direction of the tested relationship, stating that one variable is predicted to be larger or smaller than the null value while a non-directional hypothesis only validates the existence of a difference without stating its direction. 

Examples of Alternative Hypotheses  

• Starting your day with a cup of tea instead of a cup of coffee can make you more alert in the morning. 
• The growth of a plant improves significantly when it receives distilled water instead of vitamin-rich water. 

5. Logical Hypothesis

Logical hypotheses are some of the most common types of calculated assumptions in systematic investigations. It is an attempt to use your reasoning to connect different pieces in research and build a theory using little evidence. In this case, the researcher uses any data available to him, to form a plausible assumption that can be tested. 

Examples of Logical Hypothesis

• Waking up early helps you to have a more productive day. 
• Beings from Mars would not be able to breathe the air in the atmosphere of the Earth. 

6. Empirical Hypothesis  

After forming a logical hypothesis, the next step is to create an empirical or working hypothesis. At this stage, your logical hypothesis undergoes systematic testing to prove or disprove the assumption. An empirical hypothesis is subject to several variables that can trigger changes and lead to specific outcomes. 

Examples of Empirical Testing 

• People who eat more fish run faster than people who eat meat.
• Women taking vitamin E grow hair faster than those taking vitamin K.

7. Statistical Hypothesis

When forming a statistical hypothesis, the researcher examines the portion of a population of interest and makes a calculated assumption based on the data from this sample. A statistical hypothesis is most common with systematic investigations involving a large target audience. Here, it’s impossible to collect responses from every member of the population so you have to depend on data from your sample and extrapolate the results to the wider population. 

Examples of Statistical Hypothesis  

• 45% of students in Louisiana have middle-income parents. 
• 80% of the UK’s population gets a divorce because of irreconcilable differences.

What is Hypothesis Testing? 

Hypothesis testing is an assessment method that allows researchers to determine the plausibility of a hypothesis. It involves testing an assumption about a specific population parameter to know whether it’s true or false. These population parameters include variance, standard deviation, and median. 

Typically, hypothesis testing starts with developing a null hypothesis and then performing several tests that support or reject the null hypothesis. The researcher uses test statistics to compare the association or relationship between two or more variables. 

Explore: Research Bias: Definition, Types + Examples

Researchers also use hypothesis testing to calculate the coefficient of variation and determine if the regression relationship and the correlation coefficient are statistically significant.

How Hypothesis Testing Works

The basis of hypothesis testing is to examine and analyze the null hypothesis and alternative hypothesis to know which one is the most plausible assumption. Since both assumptions are mutually exclusive, only one can be true. In other words, the occurrence of a null hypothesis destroys the chances of the alternative coming to life, and vice-versa. 

Interesting: 21 Chrome Extensions for Academic Researchers in 2021

What Are The Stages of Hypothesis Testing?  

To successfully confirm or refute an assumption, the researcher goes through five (5) stages of hypothesis testing; 

• Determine the null hypothesis
• Specify the alternative hypothesis
• Set the significance level
• Calculate the test statistics and corresponding P-value
• Draw your conclusion
• Determine the Null Hypothesis

Like we mentioned earlier, hypothesis testing starts with creating a null hypothesis which stands as an assumption that a certain statement is false or implausible. For example, the null hypothesis (H0) could suggest that different subgroups in the research population react to a variable in the same way. 

• Specify the Alternative Hypothesis

Once you know the variables for the null hypothesis, the next step is to determine the alternative hypothesis. The alternative hypothesis counters the null assumption by suggesting the statement or assertion is true. Depending on the purpose of your research, the alternative hypothesis can be one-sided or two-sided. 

Using the example we established earlier, the alternative hypothesis may argue that the different sub-groups react differently to the same variable based on several internal and external factors. 

• Set the Significance Level

Many researchers create a 5% allowance for accepting the value of an alternative hypothesis, even if the value is untrue. This means that there is a 0.05 chance that one would go with the value of the alternative hypothesis, despite the truth of the null hypothesis. 

Something to note here is that the smaller the significance level, the greater the burden of proof needed to reject the null hypothesis and support the alternative hypothesis.

Explore: What is Data Interpretation? + [Types, Method & Tools]

• Calculate the Test Statistics and Corresponding P-Value 

Test statistics in hypothesis testing allow you to compare different groups between variables while the p-value accounts for the probability of obtaining sample statistics if your null hypothesis is true. In this case, your test statistics can be the mean, median and similar parameters. 

If your p-value is 0.65, for example, then it means that the variable in your hypothesis will happen 65 in100 times by pure chance. Use this formula to determine the p-value for your data: 

• Draw Your Conclusions

After conducting a series of tests, you should be able to agree or refute the hypothesis based on feedback and insights from your sample data.  

Applications of Hypothesis Testing in Research

Hypothesis testing isn’t only confined to numbers and calculations; it also has several real-life applications in business, manufacturing, advertising, and medicine. 

In a factory or other manufacturing plants, hypothesis testing is an important part of quality and production control before the final products are approved and sent out to the consumer. 

During ideation and strategy development, C-level executives use hypothesis testing to evaluate their theories and assumptions before any form of implementation. For example, they could leverage hypothesis testing to determine whether or not some new advertising campaign, marketing technique, etc. causes increased sales. 

In addition, hypothesis testing is used during clinical trials to prove the efficacy of a drug or new medical method before its approval for widespread human usage. 

What is an Example of Hypothesis Testing?

An employer claims that her workers are of above-average intelligence. She takes a random sample of 20 of them and gets the following results: 

Mean IQ Scores: 110

Standard Deviation: 15 

Mean Population IQ: 100

Step 1: Using the value of the mean population IQ, we establish the null hypothesis as 100.

Step 2: State that the alternative hypothesis is greater than 100.

Step 3: State the alpha level as 0.05 or 5% 

Step 4: Find the rejection region area (given by your alpha level above) from the z-table. An area of .05 is equal to a z-score of 1.645.

Step 5: Calculate the test statistics using this formula

Z = (110–100) ÷ (15÷√20) 

10 ÷ 3.35 = 2.99 

If the value of the test statistics is higher than the value of the rejection region, then you should reject the null hypothesis. If it is less, then you cannot reject the null. 

In this case, 2.99 > 1.645 so we reject the null. 

Importance/Benefits of Hypothesis Testing 

The most significant benefit of hypothesis testing is it allows you to evaluate the strength of your claim or assumption before implementing it in your data set. Also, hypothesis testing is the only valid method to prove that something “is or is not”. Other benefits include: 

• Hypothesis testing provides a reliable framework for making any data decisions for your population of interest. 
• It helps the researcher to successfully extrapolate data from the sample to the larger population. 
• Hypothesis testing allows the researcher to determine whether the data from the sample is statistically significant. 
• Hypothesis testing is one of the most important processes for measuring the validity and reliability of outcomes in any systematic investigation. 
• It helps to provide links to the underlying theory and specific research questions.

Criticism and Limitations of Hypothesis Testing

Several limitations of hypothesis testing can affect the quality of data you get from this process. Some of these limitations include: 

• The interpretation of a p-value for observation depends on the stopping rule and definition of multiple comparisons. This makes it difficult to calculate since the stopping rule is subject to numerous interpretations, plus “multiple comparisons” are unavoidably ambiguous. 
• Conceptual issues often arise in hypothesis testing, especially if the researcher merges Fisher and Neyman-Pearson’s methods which are conceptually distinct. 
• In an attempt to focus on the statistical significance of the data, the researcher might ignore the estimation and confirmation by repeated experiments.
• Hypothesis testing can trigger publication bias, especially when it requires statistical significance as a criterion for publication.
• When used to detect whether a difference exists between groups, hypothesis testing can trigger absurd assumptions that affect the reliability of your observation.

Connect to Formplus, Get Started Now - It's Free!

• alternative hypothesis
• alternative vs null hypothesis
• complex hypothesis
• empirical hypothesis
• hypothesis testing
• logical hypothesis
• simple hypothesis
• statistical hypothesis
• busayo.longe

You may also like:

What is Pure or Basic Research? + [Examples & Method]

Simple guide on pure or basic research, its methods, characteristics, advantages, and examples in science, medicine, education and psychology

Type I vs Type II Errors: Causes, Examples & Prevention

This article will discuss the two different types of errors in hypothesis testing and how you can prevent them from occurring in your research

Internal Validity in Research: Definition, Threats, Examples

In this article, we will discuss the concept of internal validity, some clear examples, its importance, and how to test it.

Alternative vs Null Hypothesis: Pros, Cons, Uses & Examples

We are going to discuss alternative hypotheses and null hypotheses in this post and how they work in research.

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

1.6 Application of Theories in Nursing Practice

Learning objectives.

By the end of this section, you will be able to:

• Explain the link between nursing theory and nursing knowledge
• Discuss how nursing theories help shape the future of nursing
• Recognize the use of nursing theory in the application of standards of nursing practice

In the dynamic realm of nursing practice, the application of theoretical frameworks guides the approach to patient care. This section delves into the multifaceted ways in which nursing theories are integrated into daily nursing practice, shaping the actions and decisions of healthcare professionals. As we explore the practical application of these theoretical foundations, we uncover the pivotal role they play in informing and elevating the standards of nursing care. From providing a structured decision-making framework to influencing ethical considerations and patient outcomes, nursing theories emerge as essential tools that bridge the gap between knowledge and practice.

Link Between Theory and Knowledge in Nursing

The link between theory and knowledge is the foundation on which the entire practice of nursing stands. Nursing theory acts as the compass that guides how nurses approach patient care. It also provides a structured framework shaping the knowledge that nurses acquire through education. This theoretical knowledge of established concepts forms the basis for understanding the complexities of health care and the unique role of nursing within it. As nurses apply what they have learned to real-life situations, the theoretical knowledge becomes experiential knowledge —it is the practical, hands-on learning that happens when directly caring for patients. This practical experience, in turn, feeds back into theoretical knowledge, enriching and shaping it based on the real-world scenarios encountered. The interplay between nursing theory and knowledge is dynamic and continuous, creating a cycle that ensures theoretical understanding evolves with the practical realities of patient care. This connection highlights the importance of a well-rounded, dynamic approach to knowledge that encompasses both theory and hands-on experience.

Theoretical Knowledge

Nursing theory and theoretical knowledge are interconnected and mutually beneficial. The link between them is integral to the development and advancement of the nursing profession. Nursing theory provides a framework for understanding and organizing knowledge within the field, guiding nursing practice, education, and research. Theoretical knowledge encompasses the concepts, principles, and models derived from nursing theories. These theories serve as the foundation for guiding nursing practice, influencing the way nurses approach patient care and fostering a deeper understanding of the nurse’s role.

Theoretical knowledge in nursing, derived from nursing theories, empowers nurses with a structured framework for critical thinking, enabling them to make informed decisions and provide holistic care . This knowledge not only informs nursing education, in which students learn the principles and concepts that underpin the profession, but also facilitates ongoing research efforts. Nursing theories guide research design, hypothesis formulation, and results interpretation, contributing to the growth of EBP. Moreover, the link between nursing theory and theoretical knowledge is essential for nurturing a professional identity among nurses, emphasizing the unique contributions of nursing to health care. As healthcare landscapes evolve, this theoretical foundation helps nurses adapt to new challenges while maintaining the core principles of the profession, ultimately enhancing the quality of patient care and the professionalism of the nursing field.

Experiential Knowledge

The link between nursing theory and experiential knowledge is dynamic and integral to providing safe and effective care. Imagine nursing theory as a roadmap for understanding and guiding patient care. Theoretical knowledge, born from these theories, is the foundational understanding gained through education, offering nurses a structured framework for practice. As nurses apply these theories in real-life scenarios, theoretical knowledge transforms into experiential knowledge—a hands-on, practical understanding gained through patient interactions and clinical experiences. The link between nursing theory and experiential knowledge is a dynamic loop, in which theoretical understanding both informs and evolves through hands-on experience, ensuring nursing knowledge remains adaptable and responsive to the ever-changing landscape of health care.

Reflective Skills

The connection between nursing theory and reflective skills is pivotal in fostering a culture of continuous learning and improvement within the nursing profession. Reflective skills involve the ability to critically analyze and thoughtfully contemplate one’s own experiences, actions, and decisions and then learn from them. The application of nursing theory in practice provides a rich source of experiences for nurses to reflect on. Through reflective practices, nurses can assess the alignment of their actions with theoretical principles, identify areas for improvement, and refine their approaches to patient care. This reflective process enhances self-awareness, encourages critical thinking, and contributes to ongoing professional development.

The synergy between nursing theory and knowledge gained from reflection allows nurses to bridge the gap between theory and practice, fostering a deeper understanding of the complexities inherent in healthcare delivery. As nurses engage in reflective practices, they not only refine their clinical skills but also contribute to the evolution of nursing theories by providing real-world insights and feedback. This iterative relationship between nursing theory and reflective knowledge ultimately promotes a culture of continuous improvement, ensuring that nursing practice remains evidence based, person centered, and adaptable to the ever-changing landscape of health care.

Shaping the Future Development of Nursing

Nursing theories are guiding forces that shape the future development of nursing, contributing to the profession’s evolution and ensuring its continued relevance in dynamic healthcare landscapes. These theoretical frameworks play a crucial role in defining nursing as a profession, providing a conceptual roadmap that delineates the unique identity and contributions of nurses within the healthcare spectrum. Simultaneously, they guide the establishment of professional limits and ethical boundaries, maintaining the integrity and trustworthiness of nursing practice. Additionally, nursing theories direct the recommendations for future education, ensuring that curricula and learning strategies align with the evolving needs of health care and equip nurses with the knowledge and skills required for contemporary practice. Furthermore, these theories inform the development of practice guidelines, guiding nurses in delivering person-centered care while navigating technological advancements and ethical considerations.

Defining Nursing as a Profession

Nursing theories play a pivotal role in shaping the future of nursing by contributing to the definition of nursing as a distinct and evolving profession. These theoretical frameworks provide a conceptual foundation that extends beyond the technical aspects of patient care, offering a philosophical understanding of the nursing role. By delineating the fundamental principles and values inherent in nursing practice, theories serve as a guidepost for current and future nurses, shaping their professional identity. They emphasize not only the acquisition of clinical skills but also the importance of empathy, advocacy, and holistic patient care.

Nursing theories also contribute to the nursing profession by articulating the unique body of knowledge that constitutes the nursing profession. This body of knowledge encompasses not only clinical expertise but also the ability to navigate the intricate dynamics of the nurse-patient relationship and the broader healthcare context. As nursing adapts to advancements in technology, changes in healthcare delivery, and evolving patient needs, nursing theories provide a stable foundation, ensuring that the essence of the profession remains rooted in its foundational values.

Professional Limits and Boundaries

Nursing theories play a crucial role in shaping the future of nursing by providing a framework that helps delineate professional limits and boundaries. These theoretical frameworks establish ethical guidelines and standards that guide nurses in navigating complex situations and making decisions within their scope of practice . By emphasizing the importance of ethical considerations, patient autonomy , and confidentiality , nursing theories contribute to defining the boundaries of professional conduct. As health care evolves and technology advances, nursing theories help establish limits on the integration of new practices and technologies, ensuring that ethical principle s and patient safety remain at the forefront.

Additionally, nursing theories assist in clarifying the collaborative nature of health care and the boundaries between different healthcare professions. They provide a foundation for interdisciplinary collaboration, defining the unique contributions of nursing within the broader healthcare team. This collaborative understanding is essential for shaping the future of nursing as healthcare systems become increasingly integrated and team oriented.

Nursing theories also contribute to the ongoing development of ethical guidelines and policies that regulate the profession. By engaging with ethical frameworks, nurses are better equipped to address emerging ethical challenges, set professional limits, and advocate for the well-being of their patients. This proactive approach to defining professional limits ensures that nursing remains a trusted and ethical profession in the face of evolving healthcare landscapes.

Directing Future Education Recommendations

Nursing theories significantly influence the future of nursing by serving as guiding principles for directing education recommendations. These theoretical frameworks contribute to the ongoing development and refinement of nursing education by providing a structured foundation for curricula and educational programs. By incorporating nursing theories into educational strategies, educators can impart not only technical skills but also a deeper understanding of the profession’s underlying values and principles. Nursing theories guide the identification of essential competencies, shaping the educational landscape to align with the evolving needs of health care.

In addition, nursing theories contribute to the creation of educational pathways that emphasize critical thinking, cultural competence, and ethical decision-making. As the healthcare environment becomes increasingly complex, these theoretical foundations help educators prepare future nurses to navigate diverse patient populations, emerging technologies, and evolving healthcare systems. The integration of nursing theories into education recommendations ensures that students are equipped not only with practical skills but also with the ability to think critically and adapt to dynamic healthcare challenges.

Additionally, nursing theories inform recommendations for continuous professional development, encouraging a lifelong learning mindset among nurses. By emphasizing the importance of staying abreast of theoretical advancements and incorporating EBPs, nursing theories guide education recommendations that foster a culture of continuous learning. This approach ensures that nurses are prepared to meet the changing demands of health care and contribute meaningfully to the advancement of the profession.

Directing Future Practice Guidelines

Nursing theories exert a profound influence on the future of nursing by directing and shaping the practice of the profession. These theoretical frameworks provide a structured foundation for nursing practice , guiding the delivery of person-centered care, and influencing the development of evidence-based protocols. By incorporating nursing theories into practice, nurses gain a deeper understanding of the philosophical underpinnings of their profession, leading to more thoughtful and intentional caregiving.

Nursing theories also contribute to the evolution of future practice by emphasizing holistic and patient-focused approaches. As the healthcare landscape continues to evolve, nursing theories guide practitioners in recognizing the importance of addressing not only the physical needs of patients but also their psychological, social, and cultural dimensions. This holistic perspective fosters a more comprehensive and compassionate delivery of care, aligning with the changing expectations and demographics of healthcare recipients.

Moreover, nursing theories inform the integration of technology and innovation into practice while maintaining ethical standards. As healthcare technologies advance, these theoretical foundations help nurses navigate the ethical considerations of incorporating new tools and interventions, ensuring that patient safety and well-being remain at the forefront of practice.

Additionally, nursing theories contribute to the professional autonomy and decision-making abilities of nurses. By offering a theoretical framework, these theories empower nurses to critically assess situations, make informed decisions, and advocate for the best interests of their patients. This empowerment is crucial for shaping the future of nursing practice in a healthcare environment that demands adaptability, critical thinking, and a strong ethical foundation.

Application of Theory in Nursing Practice Standards

The application of nursing theory in nursing practice standards is a fundamental aspect that enriches and informs the delivery of high-quality patient care. Nursing theories provide a theoretical framework that guides the development and refinement of practice standards, ensuring that they align with the profession’s core values and principles. By incorporating nursing theories into practice standards, healthcare organizations and professionals establish a solid foundation for decision-making, care planning, and evaluation of outcomes. For instance, a theory emphasizing person-centered care may influence practice standards to prioritize individualized and holistic approaches to patient care. Furthermore, nursing theories contribute to the establishment of ethical guidelines within practice standards, guiding nurses in navigating complex ethical dilemmas and upholding the integrity of the profession. This integration of theory into practice standards not only elevates the overall quality of care but also fosters a shared understanding among healthcare professionals, reinforcing a commitment to evidence-based and theoretically informed practice.

Clinical Safety and Procedures (QSEN)

Linking nursing theories to patient safety.

The QSEN initiative plays a crucial role in enhancing clinical safety and promoting excellence in patient care. The QSEN focuses on integrating essential competencies into nursing education and practice to ensure that nurses are equipped to deliver safe and high-quality care. When examining the link between nursing theories and clinical safety within the QSEN framework, it becomes evident that theoretical foundations contribute significantly to shaping nurses’ understanding of safety principles, risk mitigation, and delivery of patient-centered care.

• Integration of EBP: Nursing theories serve as the backbone for EBP, a fundamental component of QSEN. Theoretical frameworks guide nurses in critically evaluating evidence, making informed decisions, and implementing best practices to enhance patient safety. The integration of nursing theories into education ensures that nurses understand the theoretical underpinnings of evidence-based care, promoting a culture of continuous improvement and patient safety.
• Patient-centered care: QSEN emphasizes patient-centered care as a key competency, aligning with many nursing theories that prioritize holistic and individualized approaches. Theoretical perspectives such as Watson’s theory of human caring or Peplau’s theory of interpersonal relations provide a foundation for understanding the importance of establishing meaningful nurse-patient relationships, communicating effectively, and addressing patients’ unique needs. This theoretical grounding contributes to safer and more patient-focused clinical practices.
• Teamwork and collaboration: Nursing theories that highlight the collaborative nature of health care, such as Roy’s adaptation model or Orem’s self-care deficit nursing theory, align with QSEN’s focus on teamwork and collaboration. Theoretical frameworks guide nurses in understanding their roles within interdisciplinary teams, fostering effective communication, and promoting a collaborative approach to patient care. This integration enhances clinical safety by ensuring clear communication and coordination among healthcare team members.
• Safety competencies: The QSEN identifies safety as a core competency, and nursing theories provide the theoretical basis for understanding the principles of safety in health care. Theoretical perspectives, such as Leininger’s culture care theory or Henderson’s nursing need theory, contribute to nurses’ understanding of factors influencing patient safety, including cultural considerations and meeting patients’ basic needs. This theoretical knowledge informs safe and culturally competent care delivery.
• Informing continuous quality improvement: Many nursing theories emphasize the importance of continuous quality improvement, aligning with QSEN’s commitment to ongoing enhancement of healthcare practices. Theoretical foundations guide nurses in critically assessing their actions, reflecting on patient outcomes, and implementing changes to improve care processes. This integration supports a culture of continuous learning and adaptation, contributing to sustained clinical safety improvements.
• Informatics integration: Nursing theories contribute to the integration of informatics skills, aligning with QSEN’s focus on informatics competency. Theoretical frameworks guide nurses in understanding how to use information and technology to support decision-making, enhance communication, and improve patient outcomes. The theoretical grounding in informatics ensures that nurses can navigate and utilize health information systems effectively, promoting safe and efficient care practices. This integration supports QSEN’s goal of preparing nurses to use technology and information systems to deliver and enhance patient care while aligning with theoretical perspectives that emphasize the role of technology in health care.

Decision-Making Framework

The application of nursing theory as a decision-making framework is a cornerstone of effective and person-centered care. Nursing theories provide a structured and comprehensive foundation that guides nurses in making informed decisions across various healthcare scenarios. These theoretical frameworks help nurses analyze situations, understand patient needs, and prioritize care based on established principles. For instance, a nursing theory emphasizing the importance of environmental factors may prompt a nurse to consider how the individual’s surroundings impact their well-being.

Moreover, theories focused on the nurse-patient relationship contribute to decision-making by highlighting the significance of communication, empathy, and collaboration. By integrating nursing theory into their decision-making processes, nurses can ensure a more holistic approach that encompasses not only the physical aspects of care but also the psychological, social, and cultural dimensions. This application of nursing theory serves as a valuable tool for enhancing critical-thinking skills, fostering ethical decision-making, and elevating the overall quality of patient care.

Directing Future Research

The application of nursing theory plays a pivotal role in guiding and shaping future research endeavors within the nursing field. Nursing theories provide a conceptual framework that informs the identification of research priorities, the formulation of research questions, and the interpretation of findings. By grounding research in established nursing theories, researchers can build on a solid theoretical foundation, ensuring that studies align with the core principles and values of the profession.

Additionally, nursing theories offer a lens through which researchers can explore and understand complex phenomena, guiding the development of hypotheses and conceptual frameworks. For example, a nursing theory emphasizing patient empowerment may direct research efforts toward interventions that enhance patient engagement in their care. This application of nursing theory not only contributes to the generation of new knowledge but also ensures that research outcomes are relevant and applicable to the practical realities of nursing practice . As nursing continues to evolve, the integration of nursing theory in research remains instrumental to advancing the profession’s evidence base and promoting EBP.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/fundamentals-nursing/pages/1-introduction

• Authors: Christy Bowen, Lindsay Draper, Heather Moore
• Publisher/website: OpenStax
• Book title: Fundamentals of Nursing
• Publication date: Sep 4, 2024
• Location: Houston, Texas
• Book URL: https://openstax.org/books/fundamentals-nursing/pages/1-introduction
• Section URL: https://openstax.org/books/fundamentals-nursing/pages/1-6-application-of-theories-in-nursing-practice

© Aug 20, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Development and application of social learning theory

• PMID: 8574105
• DOI: 10.12968/bjon.1995.4.21.1263

This article traces the development of social learning theory over the last 30 years, relating the developments to clinical nursing practice. Particular attention is focused on the contribution of Albert Bandura, the American psychologist, and his work on modelling.

PubMed Disclaimer

Similar articles

• Social Learning Theory: its application in the context of nurse education. Bahn D. Bahn D. Nurse Educ Today. 2001 Feb;21(2):110-7. doi: 10.1054/nedt.2000.0522. Nurse Educ Today. 2001. PMID: 11170797 Review.
• [Albert Bandura and his work]. Guerrin B. Guerrin B. Rech Soins Infirm. 2012 Mar;(108):106-16. Rech Soins Infirm. 2012. PMID: 22616370 French.
• Charles Manson and the family: the application of sociological theories to multiple murder. Atchison AJ, Heide KM. Atchison AJ, et al. Int J Offender Ther Comp Criminol. 2011 Aug;55(5):771-98. doi: 10.1177/0306624X10371794. Epub 2010 Jun 7. Int J Offender Ther Comp Criminol. 2011. PMID: 20529993
• Albert Bandura (1925-2021). Ozer EM. Ozer EM. Am Psychol. 2022 Apr;77(3):483-484. doi: 10.1037/amp0000981. Epub 2022 Mar 3. Am Psychol. 2022. PMID: 35238591
• Practitioner review: When parent training doesn't work: theory-driven clinical strategies. Scott S, Dadds MR. Scott S, et al. J Child Psychol Psychiatry. 2009 Dec;50(12):1441-50. doi: 10.1111/j.1469-7610.2009.02161.x. Epub 2009 Sep 15. J Child Psychol Psychiatry. 2009. PMID: 19754503 Review.
• Medical Schools' Industry Interaction Policies Not Associated With Trainees' Self-Reported Behavior as Residents: Results of a National Survey. Yeh JS, Austad KE, Franklin JM, Chimonas S, Campbell EG, Avorn J, Kesselheim AS. Yeh JS, et al. J Grad Med Educ. 2015 Dec;7(4):595-602. doi: 10.4300/JGME-D-15-00029.1. J Grad Med Educ. 2015. PMID: 26692972 Free PMC article.
• Psychological and/or educational interventions for reducing alcohol consumption in pregnant women and women planning pregnancy. Stade BC, Bailey C, Dzendoletas D, Sgro M, Dowswell T, Bennett D. Stade BC, et al. Cochrane Database Syst Rev. 2009 Apr 15;(2):CD004228. doi: 10.1002/14651858.CD004228.pub2. Cochrane Database Syst Rev. 2009. PMID: 19370597 Free PMC article. Review.
• A group parent-training program: a novel approach for eating disorder management. Zucker NL, Marcus M, Bulik C. Zucker NL, et al. Eat Weight Disord. 2006 Jun;11(2):78-82. doi: 10.1007/BF03327755. Eat Weight Disord. 2006. PMID: 16809979 Clinical Trial.
• Search in MeSH

LinkOut - more resources

Full text sources.

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• BMJ Open Qual
• v.9(1); 2020

Development and application of ‘systems thinking’ principles for quality improvement

Duncan mcnab.

1 Medical Directorate, NHS Education for Scotland, Glasgow, UK

2 Institute of Health and Wellbeing, University of Glasgow, United Kingdom

Steven Shorrock

3 EUROCONTROL, Brussels, Belgium

4 University of the Sunshine Coast Sippy Downs Campus, Sippy Downs, Queensland, Australia

Associated Data

bmjoq-2019-000714supp001.pdf

bmjoq-2019-000714supp002.pdf

Introduction

‘Systems thinking’ is often recommended in healthcare to support quality and safety activities but a shared understanding of this concept and purposeful guidance on its application are limited. Healthcare systems have been described as complex where human adaptation to localised circumstances is often necessary to achieve success. Principles for managing and improving system safety developed by the European Organisation for the Safety of Air Navigation (EUROCONTROL; a European intergovernmental air navigation organisation) incorporate a ‘Safety-II systems approach’ to promote understanding of how safety may be achieved in complex work systems. We aimed to adapt and contextualise the core principles of this systems approach and demonstrate the application in a healthcare setting.

The original EUROCONTROL principles were adapted using consensus-building methods with front-line staff and national safety leaders.

Six interrelated principles for healthcare were agreed. The foundation concept acknowledges that ‘most healthcare problems and solutions belong to the system’. Principle 1 outlines the need to seek multiple perspectives to understand system safety. Principle 2 prompts us to consider the influence of prevailing work conditions—demand, capacity, resources and constraints. Principle 3 stresses the importance of analysing interactions and work flow within the system. Principle 4 encourages us to attempt to understand why professional decisions made sense at the time and principle 5 prompts us to explore everyday work including the adjustments made to achieve success in changing system conditions.

A case study is used to demonstrate the application in an analysis of a system and in the subsequent improvement intervention design.

Conclusions

Application of the adapted principles underpins, and is characteristic of, a holistic systems approach and may aid care team and organisational system understanding and improvement.

Adopting a ‘systems thinking’ approach to improvement in healthcare has been recommended as it may improve the ability to understand current work processes, predict system behaviour and design modifications to improve related functioning. 1–3 ‘Systems thinking’ involves exploring the characteristics of components within a system (eg, work tasks and technology) and how they interconnect to improve understanding of how outcomes emerge from these interactions. It has been proposed that this approach is necessary when investigating incidents where harm has, or could have, occurred and when designing improvement interventions. While acknowledged as necessary, ‘systems thinking’ is often misunderstood and there does not appear to be a shared understanding and application of related principles and approaches. 4–6 There is a need, therefore, for an accessible exposition of systems thinking.

Systems in healthcare are described as complex. In such systems it can be difficult to fully understand how safety is created and maintained. 7 Complex systems consist of many dynamic interactions between people, tasks, technology, environments (physical, social and cultural), organisational structures and external factors. 8–10 Care system components can be closely ‘coupled’ to other system elements and so change in one area can have unpredicted effects elsewhere with non-linear, cause–effect relations. 11 The nature of interactions results in unpredictable changes in system conditions (such as patient demand, staff capacity, available resources and organisational constraints) and goal conflicts (such as the frequent pressure to be efficient and thorough). 12 13 To achieve success, people frequently adapt to these system conditions and goal conflicts. But rather than being planned in advance, these adaptations are often approximate responses to the situations faced at the time. 14 Therefore, to understand safety (and other emergent outcomes such as workforce well-being) we need to look beyond the individual components of care systems to consider how outcomes (wanted and unwanted) emerge from interactions in, and adaptations to, everyday working conditions. 14

Despite the complexity of healthcare systems, we often appear to treat problems and issues in simple, linear terms. 15–17 In simple systems (eg, setting your alarm clock to wake you up) and many complicated systems (eg, a car assembly production line) ‘cause and effect’ are often linked in a predictable or linear manner. This contrasts sharply with the complexity, dynamism and uncertainty associated with much of healthcare practice. 1 7 18 For example, in a study to evaluate the impact of a comprehensive pharmacist review of patients’ medication after hospital discharge, the linear perspective suggested that this specific intervention would improve the safety and quality of medication regimens and so reduce healthcare utilisation. 19 Unexpectedly the opposite result was observed. The authors suggested that this emergent outcome may have been due to the increased number of interactions with different healthcare professionals increasing the complexity of care resulting in greater anxiety, confusion and dependence on healthcare workers.

Analyses of safety issues in healthcare routinely examine how safety is destroyed or degraded but have surprisingly little to say about how it is created and maintained. In the UK, like many parts of the world, root cause analysis is the recommended method for analysing events with an adverse outcome. 20 At its best, this should take a ‘systems approach’ to identify latent system conditions that interacted and contributed to the event and recommend evidence-based change to reduce the risk of recurrence. 20 However, we find that the results of such analyses are commonly based on linear ‘cause and effect’ assumptions and thinking. 15 16 21 22 Despite allusions to ‘root causes’, investigation approaches have a tendency to focus on single system elements such as people and/or items of equipment, rather than attempting to understand the interacting relationships and dependencies between people and other elements of the sociotechnical system from which safety performance and other outcomes in complex systems emerge. 21 By focusing on components in isolation, proposed improvement interventions risk unintended consequences in other parts of the systems and enhanced performance of the targeted component rather than the overall system. The validity of focusing on relatively infrequent, unwanted events has been questioned as it does not always reveal how wanted outcomes usually occur and may limit our learning on how to improve care. 22

Despite much related activity internationally, the impact of current safety improvement efforts in healthcare is limited. 23–25 Similar to other safety-critical industrial sectors, such as nuclear power or air traffic control, there is a growing realisation in healthcare that exploring how safety is created in complex systems may add value to existing learning and improvement efforts. The European Organisation for the Safety of Air Navigation (EUROCONTROL), a pan-European intergovernmental air navigation organisation, published a white paper, Systems Thinking for Safety: Ten Principles . 26 This sets out a way of thinking about safety in organisations that aligns with systems thinking and applies ‘Safety-II’ principles, for which there is also growing interest in healthcare. 27 This latter approach attempts to explain and potentially resolve some of the ‘intractable problems’ associated with complex systems such as those found in healthcare, which traditional safety management thinking and responses (termed Safety-I) have struggled to adequately understand and improve on. 28 The Safety-II approach aims to increase the number of events with a positive outcome by exploring and understanding how everyday work is done under different conditions and contexts. This can lead to a more informed appreciation of system functioning and complexity that may facilitate a deeper understanding of safety within systems. 29 30

In this paper, we describe principles for systems thinking in healthcare that have been adapted and contextualised from the themes within the EUROCONTROL ‘Systems Thinking for Safety’ white paper. Our goal was to provide an accessible framework to explore how work is done under different conditions to facilitate a deeper understanding of safety within systems. A case report applying these principles to healthcare systems is described to illustrate systems thinking in everyday clinical practice and how this may inform quality improvement (QI) work.

Adaptation of EUROCONTROL Systems Thinking Principles

A participatory codesign approach 31 was employed with informed stakeholders. 32 33 First, in March 2016, a 1-day systems thinking workshop was held for participants who held a variety of roles in front-line primary care (general practitioners (GP), practice nurses, practice managers and community pharmacists) and National Health Service (NHS) Scotland patient safety leaders ( table 1 ). The relevance and applicability of the EUROCONTROL white paper system principles were explored through presentations and discussion led by two experts in the field (including the original lead author of this document—SS). This was followed by a facilitated small group simulation exercise to apply the 10 principles to a range of clinical and administrative healthcare case studies ( online supplementary appendix 1 ) ( figure 1 ).

Systems Thinking for Everyday Work model.

Characteristics of attendees at Stage 1—‘Systems thinking’ workshop

Supplementary data

Second, two rounds of consensus building using the Questback online survey tool were undertaken with workshop participants in April and July 2016. 34

Finally, in May 2017, two 90 min workshops were held to test and refine the adapted principles with primary and secondary care medical appraisers (experienced medical practitioners with responsibility for the critical review of improvement and safety work performed by front-line peers).

At each stage, feedback was collected and analysed to identify themes related to applicability including wording, merging and missing principles. These themes directed the modification of the original principles and descriptors, which were then used at the next stage of development.

Throughout the process, external guidance and ‘sense-checking’ were provided by a EUROCONTROL human factors expert and lead author of the original systems thinking for safety white paper. While we believe the outputs from this work are generically applicable to all healthcare contexts, we have focused on the primary care setting for pragmatic purposes. The agreed principles are illustrated graphically in the Systems Thinking for Everyday Work (STEW) conceptual model ( figure 1 ), and detailed descriptions are provided in online supplementary appendix 2 .

Patient and public involvement

Patients and the public were not involved in the design of the study or the adaptation of the principles. The presented case study included a patient in the application of the principles to analyse the system. A service user read and commented on the manuscript and their feedback was incorporated into the final paper.

Systems Thinking for Everyday Work

The STEW principles consist of six inter-related principles ( figure 1 , tables 2 and 3 , online supplementary appendix 2 ). A fundamental, overarching conclusion is that the principles should not be viewed as isolated ideas, but instead as inter-related and interdependent concepts that can aid our understanding of complex work processes to better inform safety and improvement work by healthcare teams and organisations.

Adaptation of Systems Thinking Principles

EUROCONTROL, European Organisation for the Safety of Air Navigation.

Analysis of GP-based pharmacist work system

GP, general practitioner; STEW, Systems Thinking for Everyday Work.

Foundation concept

The foundation concept acknowledges that ‘ most healthcare problems and solutions belong to the system ’. This emphasises that the aim of applying a systems approach is to improve overall system functioning and not the functioning of one individual component within a system. For example, improving clinical assessments will not improve overall system performance unless patients can access assessments appropriately.

All systems interact with other systems, but out of necessity those analysing the system need to agree boundaries for the analysis. This may mean the GP practice building, a single hospital ward, the emergency department, a pharmacy or nursing home. Despite this, it is important to remember that external factors will influence the system under study and changes may have effects in parts of the system outside the boundary.

Multiple perspectives

Appreciate that people, at all organisational levels and regardless of responsibilities and hierarchical status, are the local experts in the work they do. Exploring the different perspectives held by these people, especially in relation to the other principles, is crucial when analysing incidents and designing and implementing change.

System conditions

Obtaining multiple perspectives allows an exploration of variability in demand and capacity, availability of resources (such as information or physical resources) and constraints (such as guidance that directs work to be performed in a particular way). These considerations can help identify leading indicators of impending trouble by identifying where demand may exceed capacity or where resources may not be available. Multiple perspectives can also help explore how work conditions affect staff well-being (eg, health, safety, motivation, job satisfaction, comfort, joy at work) and performance (eg, care quality, safety, productivity, effectiveness, efficiency).

Interactions and flow

System outputs are dependent on the constantly changing interactions between people, tasks, equipment and the wider environment. Multiple perspectives on system functioning help explore interactions to better understand the effects of actions and proposed changes on other parts of the system. Examining flow of work can help identify how these interactions and the conditions of work contribute to bottlenecks and blockages.

Understand why decisions made sense at the time

This principle directs us that, when looking back on individual, team or organisational decision-making, we should appreciate that people do what makes sense to them based on the system conditions experienced at the time (demand, capacity, resources and constraints), interactions and flow of work. It is easy (and common) to look back with hindsight to blame or judge individual components (usually humans) and recommend change such as refresher training and punitive actions. This must consider why such decisions were made, or change is unlikely to be effective. The same conditions may occur again, and the same decision may need to be made to continue successful system functioning. By exploring why decisions were made, we move beyond blaming ‘human error’ which can help promote a ‘Just Culture’—where staff are not punished for actions that are in keeping with their experience and training and which were made to cope with the work conditions faced at the time. 35

Performance variability

As work conditions and interactions change rapidly and often in an unpredicted manner, people adapt what they do to achieve successful outcomes. They make trade-offs, such as efficiency thoroughness trade-offs, and use workarounds to cope with the conditions they face. In retrospect these could be seen as ‘errors’, but are often adaptations used to cope with unplanned or unexpected system conditions. They result in a difference between work-as-done and work-as-imagined and define everyday work from which outcomes, both good and bad, emerge.

Case report

The included case report describes the practical application of these principles to understand work within a system and the subsequent design of organisational change ( table 3 ). The presented details are a small part of a larger project in which the authors (DM, PB and SL) were involved. The new appointment of a health board employed pharmacist to a general practice had not had the anticipated impact and there had been unexpected effects. The GPs had hoped for a greater reduction in workload quantity, the health board had hoped for increased formulary compliance and there had been increased workload in secondary care.

Traditional ways of exploring this problem may include working backwards from the problem to identify an area for improvement. In this case, further training of the pharmacist may have been suggested and targets may have been introduced in relation to workload or formulary compliance. However, without understanding why the pharmacist worked this way, it is likely any retraining or change would be ineffective. The STEW principles provided a framework to analyse the problem from a systems perspective, understand what influenced the pharmacist’s decisions and explore the effects of these decisions elsewhere in the system. Obtaining multiple perspectives identified that the pharmacist had to trade off between competing goals (productivity vs thoroughness including safety and formulary compliance). The application of the principles identified how pharmacists varied their approach to increase productivity while remaining safe. Learning from this everyday work helped bring work-as-done and work-as-imagined closer and several changes to improve system performance were identified and implemented.

Access to hospital electronic prescribing information

This ensured pharmacists had the information needed to complete the task ( System condition—resources ). It also reduced work in other sectors ( Interactions ) and increased the efficiency of task completion and so reduced delays for patients ( Flow ).

Work scheduling

The timetable for the week was changed to prioritise other prescribing tasks at the start of the week and complete medication reconciliation later in the week ( System condition—capacity/demand ). Through discussion of system conditions, the pharmacist identified that certain discharges took longer to complete, resulted in further contact with the practice (with a resultant increased GP workload) or had an increased risk of patient harm. Discharges that included these factors were prioritised and completed early in the week in attempt to mitigate these problems.

Protocols were changed to have minimum specification to allow local adaptation by pharmacists ( System conditions—constraints ). This supported the pharmacists to employ a variety of responses dependent on the context ( Performance Variability ) which reduced pharmacists’ concerns of blame if they did not follow the protocol ( Understand why decision made sense ). For example, after a short admission where it was unlikely medication was changed, pharmacists did not need to contact secondary care regarding medication not recorded on the discharge letter ( Understand why decision made sense ). If they felt they did have to check, the option of contacting the patient was included. Similarly, the need to contact all patients after discharge was removed. Pharmacists could use other options such as contacting the community pharmacy if more appropriate ( Performance Variability ).

Pharmacist mentoring

Regular GP mentoring sessions were included as pharmacists’ found discussing cases with GPs allowed them to consider the benefits and potential problems of their actions in other parts of the system (Interactions and Performance Variability ). For example, not limiting the number of times certain medication can be issued but instead ensuring practice systems for monitoring are used. This also allowed them to consider when they needed to be more thorough at the expense of efficiency ( Performance Variability ), for example, when there were leading indicators of problems such as high-risk medication.

This paper describes the adaptation and redesign of previously developed system principles for generic application in healthcare settings. The STEW principles underpin and are characteristic of a holistic systems approach. The case report demonstrates application of the principles to analyse a care system and to subsequently design change through understanding current work processes, predicting system behaviour and designing modifications to improve system performance.

We propose that the STEW principles can be used as a framework for teams to analyse, learn and improve from unintended outcomes, reports of excellent care and routine everyday work ‘hassles’. 36 37 The overall focus is on team and organisational learning by, for example, small group discussion to promote a deep understanding of ‘how everyday work is actually done’ (rather than just fixating on things that go wrong). This allows an exploration of the system conditions that result in the need for people to vary how they work; the identification and sharing of successful adaptations and an understanding of the effect of adaptations elsewhere in the system (mindful adaptation). From this, we can decide if variation is useful (and thus support staff in doing this effectively) or unwanted (and system conditions can then be considered to try to damp variation). These discussions can help reconcile work-as-done and work-as-imagined . Although, as conditions change unpredictably, new ways of working will continue to evolve and so we must continue to explore and share learning from everyday work, not just when something goes wrong.

The focus of safety efforts, in incident investigation and other QI activity, is often on identifying things that have gone wrong and implementing change to prevent ‘error’ recurring. 20 The focus is often on the ‘root causes’ of adverse events or categorising events most likely to cause systems to fail (eg, using Pareto charts). 20 38 This linear ‘cause and effect’ thinking can lead to single components, deemed to be the ‘cause’ of the unwanted event or care problem, being prioritised for improvement. Although this may improve the performance of that component it may not improve overall system functioning and, due to the complex interactions in healthcare systems, may generate unwanted unintended consequences. The principles promote examining and treating the relevant system as a whole which may strengthen the way we conduct incident investigation and how we design QI projects.

To successfully align corrective actions or improvement interventions with contributing factors, and therefore ensure actions have the desired effect, a deep understanding of everyday work is essential. 39 Methods such as process mapping are often promoted to explore how systems work which, when used properly, can be a useful method to aid healthcare improvers. To more closely model and understand work-as-done , the STEW principles could be considered to show the influences on components that affect performance such as feedback loops, coupling to other components and internal and external influences.

The STEW principles may also support another commonly used QI method: Plan, Do, Study, Act cycles. 40 It has been suggested that more in-depth work is often required in the planning and study stages of improvement activity, especially when dealing with complex problems. 40 The application of the principles may help explore factors that will influence change (such as resources, interactions with other parts of the systems and personal and organisational goals). Similarly, during the study phase, the principles can help explore how system properties prompted people to act the way they did. This level of understanding can then inform further iterative cycles.

Patient care is often delivered by teams across interfaces of care which further increases complexity. 41 It is estimated that only around half to three-quarters of actions recommended after incident analysis are implemented. 21 Although this is often due to a lack of shared learning and local action plans and involvement of key stakeholders, 21 those investigating such cases may feel unable to influence change in such a complex environment. This may result on a focus on what is perceived as manageable or feasible changes to single processes. Obtaining multiple perspective on work and improvement encourages a team-based approach to learning and change but systems are still required to ensure learning and action plans are shared. Although the principles have been used in incident investigation and to influence organisational change across care interfaces, simply introducing a set of principles alone will not improve the likelihood of the implementation of effective system-level change. 42 43 Training on, and evaluation of, the application of the principles is required.

Understanding how safety is created and maintained must involve more than examining when it fails. Improvement interventions often aim to standardise and simplify current processes. Although these approaches are important, in a resource-limited environment, it will never be possible to implement organisational change to fix all system problems. Even if this was possible, as systems evolve with new treatments and technology, conditions will emerge that have not been considered. To optimise success in complex systems, the contribution of humans to creating safety needs to be explored, understood and enhanced. 44 Human adaptation is always required to ensure safe working and needs to be understood, appreciated and supported. Studying systems using the principles may support workers who make such adaptations to be more mindful of wider system effects.

There is growing interest in healthcare in how we can learn more from how people create safety. The Learning from Excellence movement promotes learning and improvement from the analysis of peer-reported episodes of excellent care and positive deviancy aims to identify how some people excel despite facing the same constraints as others. 36 45 The Safety-II systems approach that influenced these principles is similar in that it focuses on how people help to create safety by adapting to unplanned system factors and interactions.

By understanding why decisions are made, the application of the principles supports the development of a ‘Just Culture’—indeed this was one of EUROCONTROL’s original principles and was incorporated into the principle, ‘Understand why decisions make sense at the time’. A ‘Just Culture’ has been described as ‘a culture of trust, learning and accountability’, where people are willing to report incidents where something has gone wrong, as they know it will inform learning to improve care and not be used to assign blame inappropriately. 35 Our approach aims to avoid unwarranted blame and increase healthcare staff support and learning when something has gone wrong. 46 47 Furthermore, application of the principles may empower staff and patients to not just report incidents but contribute to analysis and become integral parts of the improvement process through coproduction of safer systems. Obtaining the perspective of the patient when applying the principles is critical to understanding and improving systems as they are often the only constant when care crosses interfaces. This type of approach to improvement is strongly promoted and may avoid short-sighted responses to patient safety incidents (eg, refresher training or new protocols) and result in the design of better, and more cost-effective care systems. 48

Alternative methods exist for modelling and understanding complex systems, such as the Functional Resonance Analysis Method, 49 and a complex systems approach is used in accident models such as the Systems Theoretic Accident Modelling and Processes 50 and AcciMAPs. 51 These robust methods for system analysis are difficult for front-line teams to implement without specialised training. 29 The principles, on the other hand, were designed with front-line healthcare workers in order to allow non-experts to be able to adopt this type of thinking to understand and improve systems. The influence of conditions of work, including organisational and external factors, on safety has been appreciated for some time and is included in other models used in healthcare to explore safety in complex systems. 52–54 The Systems Engineering Initiative for Patient Safety (SEIPS) model is arguably one of the best known systems-based frameworks in healthcare. 53 While this model promotes seeking multiple perspectives to describe the interactions between components, the STEW principles focus on how these interactions influence the way work is done and thus may complement the use of the SEIPS model.

Strength and limitations

Any consensus method can produce an agreed outcome, but that does not mean these are wholly adequate in terms of validity, feasibility or transferability. Only 15 participants were involved in the initial development with 32 more in workshops; however, a wide range of professions with significant patient safety and QI experience were recruited. The appraiser workshop was attended by both primary and secondary care doctors, and other staff groups. Their comments were used to further refine the principles, but no attempt was made to assess their agreement on the importance and applicability of principles. The principles have not been shown in practice to improve performance, and further research and evaluation of their application in various sectors of healthcare is needed.

Systems thinking is essential for examining and improving healthcare safety and performance, but a shared understanding and application of the concept is not well developed among front-line staff, healthcare improvers, leaders, policymakers, the media and the general public. It is a complicated topic and requires an understandable framework for practical application by the care workforce. The developed principles may aid a deeper exploration of system safety in healthcare as part of learning from problematic situations, everyday work and excellent practices. They may also inform more effective design of local improvement interventions. Ultimately, the principles help define what a ‘systems approach’ actually entails in a practical sense within the healthcare context. ​

Research ethics

Under UK ‘Governance Arrangements for Research Ethics Committees’, ethical research committee review is not required for service evaluation or research which, for example, seeks to elicit the views, experiences and knowledge of healthcare professionals on a given subject area. 55 Similarly ‘service evaluation’ that involves NHS staff recruited as research participants by virtue of their professional roles also does not require ethical review from an established NHS research ethics committee.

Acknowledgments

The authors thank all those who contributed to the adaptation of the principles and Michael Cannon for his comments from a service user’s perspective.

Twitter: @duncansmcnab, @pbnes

Contributors: DM, JM and PB conceived the project. SS developed the original principles and led the consensus building workshop. DM and SL collected the data. DM, SL, SS, JM and PB analysed the feedback to adapt the principles. DM drafted the original report and SL, SS and JM revised and agreed on the final manuscript.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Patient and public involvement: Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research.

Patient consent for publication: Not required.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement: Data are available upon reasonable request. Data are available upon request relating to the stages of the consensus building process.

How to Implement Hypothesis-Driven Development

Remember back to the time when we were in high school science class. Our teachers had a framework for helping us learn – an experimental approach based on the best available evidence at hand. We were asked to make observations about the world around us, then attempt to form an explanation or hypothesis to explain what we had observed. We then tested this hypothesis by predicting an outcome based on our theory that would be achieved in a controlled experiment – if the outcome was achieved, we had proven our theory to be correct.

We could then apply this learning to inform and test other hypotheses by constructing more sophisticated experiments, and tuning, evolving or abandoning any hypothesis as we made further observations from the results we achieved.

Experimentation is the foundation of the scientific method, which is a systematic means of exploring the world around us. Although some experiments take place in laboratories, it is possible to perform an experiment anywhere, at any time, even in software development.

Practicing  Hypothesis-Driven Development  is thinking about the development of new ideas, products and services – even organizational change – as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.

We need to change our mindset to view our proposed solution to a problem statement as a hypothesis, especially in new product or service development – the market we are targeting, how a business model will work, how code will execute and even how the customer will use it.

We do not do projects anymore, only experiments. Customer discovery and Lean Startup strategies are designed to test assumptions about customers. Quality Assurance is testing system behavior against defined specifications. The experimental principle also applies in Test-Driven Development – we write the test first, then use the test to validate that our code is correct, and succeed if the code passes the test. Ultimately, product or service development is a process to test a hypothesis about system behaviour in the environment or market it is developed for.

The key outcome of an experimental approach is measurable evidence and learning.

Learning is the information we have gained from conducting the experiment. Did what we expect to occur actually happen? If not, what did and how does that inform what we should do next?

In order to learn we need use the scientific method for investigating phenomena, acquiring new knowledge, and correcting and integrating previous knowledge back into our thinking.

As the software development industry continues to mature, we now have an opportunity to leverage improved capabilities such as Continuous Design and Delivery to maximize our potential to learn quickly what works and what does not. By taking an experimental approach to information discovery, we can more rapidly test our solutions against the problems we have identified in the products or services we are attempting to build. With the goal to optimize our effectiveness of solving the right problems, over simply becoming a feature factory by continually building solutions.

The steps of the scientific method are to:

• Make observations
• Formulate a hypothesis
• Design an experiment to test the hypothesis
• State the indicators to evaluate if the experiment has succeeded
• Conduct the experiment
• Evaluate the results of the experiment
• Accept or reject the hypothesis
• If necessary, make and test a new hypothesis

Using an experimentation approach to software development

We need to challenge the concept of having fixed requirements for a product or service. Requirements are valuable when teams execute a well known or understood phase of an initiative, and can leverage well understood practices to achieve the outcome. However, when you are in an exploratory, complex and uncertain phase you need hypotheses.

Handing teams a set of business requirements reinforces an order-taking approach and mindset that is flawed.

Business does the thinking and ‘knows’ what is right. The purpose of the development team is to implement what they are told. But when operating in an area of uncertainty and complexity, all the members of the development team should be encouraged to think and share insights on the problem and potential solutions. A team simply taking orders from a business owner is not utilizing the full potential, experience and competency that a cross-functional multi-disciplined team offers.

Framing hypotheses

The traditional user story framework is focused on capturing requirements for what we want to build and for whom, to enable the user to receive a specific benefit from the system.

As A…. <role>

I Want… <goal/desire>

So That… <receive benefit>

Behaviour Driven Development (BDD) and Feature Injection  aims to improve the original framework by supporting communication and collaboration between developers, tester and non-technical participants in a software project.

In Order To… <receive benefit>

As A… <role>

When viewing work as an experiment, the traditional story framework is insufficient. As in our high school science experiment, we need to define the steps we will take to achieve the desired outcome. We then need to state the specific indicators (or signals) we expect to observe that provide evidence that our hypothesis is valid. These need to be stated before conducting the test to reduce biased interpretations of the results. 

If we observe signals that indicate our hypothesis is correct, we can be more confident that we are on the right path and can alter the user story framework to reflect this.

Therefore, a user story structure to support Hypothesis-Driven Development would be;

We believe < this capability >

What functionality we will develop to test our hypothesis? By defining a ‘test’ capability of the product or service that we are attempting to build, we identify the functionality and hypothesis we want to test.

Will result in < this outcome >

What is the expected outcome of our experiment? What is the specific result we expect to achieve by building the ‘test’ capability?

We will know we have succeeded when < we see a measurable signal >

What signals will indicate that the capability we have built is effective? What key metrics (qualitative or quantitative) we will measure to provide evidence that our experiment has succeeded and give us enough confidence to move to the next stage.

The threshold you use for statistically significance will depend on your understanding of the business and context you are operating within. Not every company has the user sample size of Amazon or Google to run statistically significant experiments in a short period of time. Limits and controls need to be defined by your organization to determine acceptable evidence thresholds that will allow the team to advance to the next step.

For example if you are building a rocket ship you may want your experiments to have a high threshold for statistical significance. If you are deciding between two different flows intended to help increase user sign up you may be happy to tolerate a lower significance threshold.

The final step is to clearly and visibly state any assumptions made about our hypothesis, to create a feedback loop for the team to provide further input, debate and understanding of the circumstance under which we are performing the test. Are they valid and make sense from a technical and business perspective?

Hypotheses when aligned to your MVP can provide a testing mechanism for your product or service vision. They can test the most uncertain areas of your product or service, in order to gain information and improve confidence.

Examples of Hypothesis-Driven Development user stories are;

Business story

We Believe That increasing the size of hotel images on the booking page

Will Result In improved customer engagement and conversion

We Will Know We Have Succeeded When we see a 5% increase in customers who review hotel images who then proceed to book in 48 hours.

It is imperative to have effective monitoring and evaluation tools in place when using an experimental approach to software development in order to measure the impact of our efforts and provide a feedback loop to the team. Otherwise we are essentially blind to the outcomes of our efforts.

In agile software development we define working software as the primary measure of progress.

By combining Continuous Delivery and Hypothesis-Driven Development we can now define working software and validated learning as the primary measures of progress.

Ideally we should not say we are done until we have measured the value of what is being delivered – in other words, gathered data to validate our hypothesis.

Examples of how to gather data is performing A/B Testing to test a hypothesis and measure to change in customer behaviour. Alternative testings options can be customer surveys, paper prototypes, user and/or guerrilla testing.

One example of a company we have worked with that uses Hypothesis-Driven Development is  lastminute.com . The team formulated a hypothesis that customers are only willing to pay a max price for a hotel based on the time of day they book. Tom Klein, CEO and President of Sabre Holdings shared  the story  of how they improved conversion by 400% within a week.

Combining practices such as Hypothesis-Driven Development and Continuous Delivery accelerates experimentation and amplifies validated learning. This gives us the opportunity to accelerate the rate at which we innovate while relentlessly reducing cost, leaving our competitors in the dust. Ideally we can achieve the ideal of one piece flow: atomic changes that enable us to identify causal relationships between the changes we make to our products and services, and their impact on key metrics.

As Kent Beck said, “Test-Driven Development is a great excuse to think about the problem before you think about the solution”. Hypothesis-Driven Development is a great opportunity to test what you think the problem is, before you work on the solution.

How can you achieve faster growth?

Bronfenbrenner’s Ecological Systems Theory

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

Bronfenbrenner’s ecological systems theory posits that an individual’s development is influenced by a series of interconnected environmental systems, ranging from the immediate surroundings (e.g., family) to broad societal structures (e.g., culture).

These systems include the microsystem, mesosystem, exosystem, macrosystem, and chronosystem, each representing different levels of environmental influences on an individual’s growth and behavior.

Key Takeaways

• The theory views child development as a complex system of relationships affected by multiple levels of the surrounding environment.
• Bronfenbrenner divided the environment into five systems: microsystem, mesosystem, exosystem, macrosystem, and chronosystem.
• The microsystem is the most influential level, encompassing the child’s immediate environment such as family and school.
• The theory has significant implications for educational practice and understanding diverse developmental contexts.

The Five Ecological Systems

Bronfenbrenner (1977) suggested that the child’s environment is a nested arrangement of structures, each contained within the next. He organized them in order of how much of an impact they have on a child.

He named these structures the microsystem, mesosystem, exosystem, macrosystem and the chronosystem.

Because the five systems are interrelated, the influence of one system on a child’s development depends on its relationship with the others.

1. The Microsystem

The microsystem is the first level of Bronfenbrenner’s theory and is the things that have direct contact with the child in their immediate environment.

It includes the child’s most immediate relationships and environments. For example, a child’s parents, siblings, classmates, teachers, and neighbors would be part of their microsystem.

Relationships in a microsystem are bi-directional, meaning other people can influence the child in their environment and change other people’s beliefs and actions. The interactions the child has with these people and environments directly impact development.

The child is not just a passive recipient but an active contributor in these bidirectional interactions.

Example: Supportive parents who read to their child and provide educational activities may positively influence cognitive and language skills. Or, children with friends who bully them at school might develop self-esteem issues. 

2. The Mesosystem

The mesosystem is where a person’s individual microsystems do not function independently but are interconnected and assert influence upon one another.

The mesosystem involves interactions between different microsystems in the child’s life. These interactions can have significant impacts on the child’s development.

Example: A child whose parents are actively involved in their school life, such as attending parent-teacher conferences and volunteering for school events, may perform better academically.

This is because the interaction between the family microsystem and the school microsystem (forming the mesosystem) creates a supportive environment for learning.

Another example could be the interaction between a child’s peer group and family. If a child’s friends value academic achievement, this attitude might influence the child’s behavior at home, leading to more time spent on homework and studying.

3. The Exosystem

The exosystem is a component of the ecological systems theory developed by Urie Bronfenbrenner in the 1970s.

It incorporates other formal and informal social structures such as local governments, friends of the family, and mass media.

While not directly interacting with the child, the exosystem still influences the microsystems. 

Example: A parent’s workplace policies can significantly affect a child’s development. If a company offers flexible working hours or work-from-home options, parents might have more time to spend with their children, positively impacting the child’s emotional development and family relationships.

Another example could be local government decisions. If a city council decides to close down a community center or library due to budget cuts, this could limit a child’s access to educational resources and after-school activities, potentially affecting their academic and social development.

4. The Macrosystem

The macrosystem focuses on how cultural elements affect a child’s development, consisting of cultural ideologies, attitudes, and social conditions that children are immersed in.

Beliefs about gender roles, individualism, family structures, and social issues establish norms and values that permeate a child’s microsystems. 

The macrosystem differs from the previous ecosystems as it does not refer to the specific environments of one developing child but the already established society and culture in which the child is developing.

Example: In a society that highly values individual achievement, children might be encouraged to be more competitive and self-reliant.

This could influence parenting styles in the microsystem, with parents focusing more on personal accomplishments and independence.

Conversely, in a culture that emphasizes collective harmony, children might be raised to prioritize group needs over individual desires.

This could manifest in the microsystem as parents encouraging more cooperative play and shared decision-making among siblings.

5. The Chronosystem

The fifth and final level of Bronfenbrenner’s ecological systems theory is known as the chronosystem.

The chronosystem relates to shifts and transitions over the child’s lifetime. These environmental changes can be predicted, like starting school, or unpredicted, like parental divorce or changing schools when parents relocate for work, which may cause stress.

Aging itself interacts with shifting social expectations over the lifespan within the chronosystem.

How children respond to expected and unexpected life transitions depends on the support of their ecological systems.

Example: The introduction of widespread internet access and social media represents a significant chronosystem change for many children.

This technological shift has altered how children interact with peers, access information, and spend their leisure time, potentially affecting their social skills, cognitive development, and even sleep patterns.

Another example could be a major historical event like a global pandemic.

Children growing up during such a time might experience disruptions in their education (shift to online learning), changes in family dynamics (parents working from home), and altered social interactions (social distancing), all of which can have long-lasting effects on their development.

The Bioecological Model

It is important to note that Bronfenbrenner (1994) later revised his theory and instead named it the ‘Bioecological model’.

Bronfenbrenner became more concerned with the proximal development processes, meaning the enduring and persistent forms of interaction in the immediate environment.

His focus shifted from environmental influences to developmental processes individuals experience over time.

‘…development takes place through the process of progressively more complex reciprocal interactions between an active, evolving biopsychological human organism and the persons, objects, and symbols in its immediate external environment.’ ( Bronfenbrenner, 1995 ).

Bronfenbrenner also suggested that to understand the effect of these proximal processes on development, we have to focus on the person, context, and developmental outcome, as these processes vary and affect people differently.

While his original ecological systems theory emphasized the role of environmental systems, his later bioecological model focused more closely on micro-level interactions.

The bioecological shift highlighted reciprocal processes between the actively evolving individual and their immediate settings. This represented an evolution in Bronfenbrenner’s thinking toward a more dynamic developmental process view.

However, the bioecological model still acknowledged the broader environmental systems from his original theory as an important contextual influence on proximal processes.

The bioecological focus on evolving person-environment interactions built upon the foundation of his ecological systems theory while bringing developmental processes to the forefront.

Classroom Application

The Ecological Systems Theory has been used to link psychological and educational theory to early educational curriculums and practice. The developing child is at the center of the theory, and all that occurs within and between the five ecological systems is done to benefit the child in the classroom.

• According to the theory, teachers and parents should maintain good communication with each other and work together to benefit the child and strengthen the development of the ecological systems in educational practice.
• Teachers should also understand the situations their students’ families may be experiencing, including social and economic factors that are part of the various systems.
• According to the theory, if parents and teachers have a good relationship, this should positively shape the child’s development.
• Likewise, the child must be active in their learning, both academically and socially. They must collaborate with their peers and participate in meaningful learning experiences to enable positive development.

There are lots of studies that have investigated the effects of the school environment on students. Below are some examples:

Lippard  et al. (2017) conducted a study to test Bronfenbrenner’s theory. They investigated the teacher-child relationships through teacher reports and classroom observations.

They found that these relationships were significantly related to children’s academic achievement and classroom behavior, suggesting that these relationships are important for children’s development and supports the Ecological Systems Theory.

Wilson et al. (2002) found that creating a positive school environment through a school ethos valuing diversity has a positive effect on students’ relationships within the school. Incorporating this kind of school ethos influences those within the developing child’s ecological systems.

Langford et al. (2014) found that whole-school approaches to the health curriculum can positively improve educational achievement and student well-being. Thus, the development of the students is being affected by the microsystems.

Critical Evaluation

Bronfenbrenner’s model quickly became very appealing and accepted as a useful framework for psychologists, sociologists, and teachers studying child development.

The ecological systems theory is thought to provide a holistic approach that includes all the systems children and their families are involved in, reflecting the dynamic nature of actual family relationships.

Paat (2013) considers how Bronfenbrenner’s theory is useful when it comes to the development of immigrant children. They suggest that immigrant children’s experiences in the various ecological systems are likely to be shaped by their cultural differences.

Understanding these children’s ecology can aid in strengthening social work service delivery for these children.

Limitations

A limitation of the Ecological Systems Theory is that there is limited research examining the mesosystems, mainly the interactions between neighborhoods and the family of the child. Therefore, the extent to which these systems can shape child development is unclear.

Another limitation of Bronfenbrenner’s theory is that it is difficult to empirically test the theory. The studies investigating the ecological systems may establish an effect, but they cannot establish whether the systems directly cause such effects.

Furthermore, this theory can lead to assumptions that those who do not have strong and positive ecological systems lack in development.

Whilst this may be true in some cases, many people can still develop into well-rounded individuals without positive influences from their ecological systems.

For instance, it is not true to say that all people who grow up in poverty-stricken areas of the world will develop negatively. Similarly, if a child’s teachers and parents do not get along, some children may not experience any negative effects if it does not concern them.

As a result, people should try to avoid making broad assumptions about individuals using this theory.

Evolution and Relevance of Bronfenbrenner’s Theory in the 21st Century

Bronfenbrenner’s theory of human development has undergone significant evolution since its inception in the 1970s, raising questions about its current relevance and application.

Initially conceptualized as an ecological model focused primarily on contextual influences, it matured into a more sophisticated bioecological model emphasizing the critical role of proximal processes in development.

The mature version of the theory, often referred to as the bioecological model, places proximal processes at its core.

These processes are defined as “enduring forms of interaction in the immediate environment” and are considered the primary engines of development.

Central to the mature theory is the Process-Person-Context-Time (PPCT) model . This model emphasizes the interplay between four key elements:

• Process: The core proximal processes driving development
• Person: Individual characteristics that influence these processes
• Context: The environmental systems in which development occurs
• Time: The temporal aspect of development, including both individual life course and historical time

Despite these advancements, the theory’s relevance in the 21st century has been a subject of debate. Kelly and Coughlan (2019) found significant links between Bronfenbrenner’s ecological systems theory and contemporary frameworks for youth mental health recovery.

Their research suggests that the components of mental health recovery are embedded in an “ecological context of influential relationships,” aligning with Bronfenbrenner’s emphasis on the importance of interconnected environmental systems.

However, the rapid technological advancements of the 21st century have raised questions about how well Bronfenbrenner’s theory accommodates these changes.

The theory’s relevance is further challenged by common misapplications in contemporary research.

Many scholars continue to apply outdated versions or misinterpret key concepts when claiming to use Bronfenbrenner’s theory, as pointed out by other scholars .

These misapplications often involve focusing solely on contextual influences without considering proximal processes, or failing to account for the time dimension in research designs.

Despite these challenges, Bronfenbrenner’s theory remains a valuable framework for understanding human development in the 21st century.

Its comprehensive nature allows for the examination of development in various contexts and across different life stages.

The theory’s emphasis on the interplay between individual characteristics, environmental influences, and temporal factors provides a nuanced approach to understanding the complexities of modern human development.

To maintain its relevance, researchers and practitioners must understand the theory’s evolution and apply it correctly.

This involves recognizing the centrality of proximal processes, considering the role of technology in developmental contexts, and designing studies that capture the dynamic nature of development over time.

By adapting the theory to include modern contexts while maintaining its core principles, Bronfenbrenner’s bioecological model can continue to provide valuable insights into human development in the 21st century and beyond.

Neo-ecological theory

Navarro & Tudge (2022) proposed the neo-ecological theory, an adaptation of the bioecological theory. Below are their main ideas for updating Bronfenbrenner’s theory to the technological age:

• Virtual microsystems should be added as a new type of microsystem to account for online interactions and activities. Virtual microsystems have unique features compared to physical microsystems, like availability, publicness, and asychnronicity.
• The macrosystem (cultural beliefs, values) is an important influence, as digital technology has enabled youth to participate more in creating youth culture and norms.
• Proximal processes, the engines of development, can now happen through complex interactions with both people and objects/symbols online. So, proximal processes in virtual microsystems need to be considered.

Background On Urie Bronfenbrenner

Urie Bronfenbrenner was born in Moscow, Russia, in 1917 and experienced turmoil in his home country as a child before immigrating to the United States at age 6.

Witnessing the difficulties faced by children during the unrest and rapid social change in Russia shaped his ideas about how environmental factors can influence child development.

Bronfenbrenner went on to earn a Ph.D. in developmental psychology from the University of Michigan in 1942.

At the time, most child psychology research involved lab experiments with children briefly interacting with strangers.

Bronfenbrenner criticized this approach as lacking ecological validity compared to real-world settings where children live and grow. For example, he cited Mary Ainsworth’s 1970 “Strange Situation” study , which observed infants with caregivers in a laboratory.

Bronfenbrenner argued that these unilateral lab studies failed to account for reciprocal influence between variables or the impact of broader environmental forces.

His work challenged the prevailing views by proposing that multiple aspects of a child’s life interact to influence development.

In the 1970s, drawing on foundations from theories by Vygotsky, Bandura, and others acknowledging environmental impact, Bronfenbrenner articulated his groundbreaking Ecological Systems Theory.

This framework mapped children’s development across layered environmental systems ranging from immediate settings like family to broad cultural values and historical context.

Bronfenbrenner’s ecological perspective represented a major shift in developmental psychology by emphasizing the role of environmental systems and broader social structures in human development.

The theory sparked enduring influence across many fields, including psychology, education, and social policy.

Frequently Asked Questions

What is the main contribution of bronfenbrenner’s theory.

The Ecological Systems Theory has contributed to our understanding that multiple levels influence an individual’s development rather than just individual traits or characteristics.

Bronfenbrenner contributed to the understanding that parent-child relationships do not occur in a vacuum but are embedded in larger structures.

Ultimately, this theory has contributed to a more holistic understanding of human development, and has influenced fields such as psychology, sociology, and education.

What could happen if a child’s microsystem breaks down?

If a child experiences conflict or neglect within their family, or bullying or rejection by their peers, their microsystem may break down. This can lead to a range of negative outcomes, such as decreased academic achievement, social isolation, and mental health issues.

Additionally, if the microsystem is not providing the necessary support and resources for the child’s development, it can hinder their ability to thrive and reach their full potential.

How can the Ecological System’s Theory explain peer pressure?

The ecological systems theory explains peer pressure as a result of the microsystem (immediate environment) and mesosystem (connections between environments) levels.

Peers provide a sense of belonging and validation in the microsystem, and when they engage in certain behaviors or hold certain beliefs, they may exert pressure on the child to conform. The mesosystem can also influence peer pressure, as conflicting messages and expectations from different environments can create pressure to conform.

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood . Child development, 45 (1), 1-5.

Bronfenbrenner, U. (1977). Toward an experimental ecology of human development . American psychologist, 32 (7), 513.

Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Harvard University Press.

Bronfenbrenner, U. (1995). Developmental ecology through space and time: A future perspective .

Bronfenbrenner, U., & Evans, G. W. (2000). Developmental science in the 21st century: Emerging questions, theoretical models, research designs and empirical findings . Social development, 9 (1), 115-125.

Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualised: A bio-ecological model . Psychological Review, 10 (4), 568–586.

Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmental processes. In W. Damon & R. M. Lerner (Eds.),  Handbook of child psychology: Theoretical models of human development  (5th ed., pp. 993–1028). John Wiley & Sons, Inc..

Hayes, N., O’Toole, L., & Halpenny, A. M. (2017). Introducing Bronfenbrenner: A guide for practitioners and students in early years education . Taylor & Francis.

Kelly, M., & Coughlan, B. (2019). A theory of youth mental health recovery from a parental perspective . Child and Adolescent Mental Health, 24 (2), 161-169.

Langford, R., Bonell, C. P., Jones, H. E., Pouliou, T., Murphy, S. M., Waters, E., Komro, A. A., Gibbs, L. F., Magnus, D. & Campbell, R. (2014). The WHO Health Promoting School framework for improving the health and well‐being of students and their academic achievement . Cochrane database of systematic reviews, (4) .

Leventhal, T., & Brooks-Gunn, J. (2000). The neighborhoods they live in: the effects of neighborhood residence on child and adolescent outcomes . Psychological Bulletin, 126 (2), 309.

Lippard, C. N., La Paro, K. M., Rouse, H. L., & Crosby, D. A. (2018, February). A closer look at teacher–child relationships and classroom emotional context in preschool . In Child & Youth Care Forum 47 (1), 1-21.

Navarro, J. L., & Tudge, J. R. (2022). Technologizing Bronfenbrenner: neo-ecological theory.  Current Psychology , 1-17.

Paat, Y. F. (2013). Working with immigrant children and their families: An application of Bronfenbrenner’s ecological systems theory . Journal of Human Behavior in the Social Environment, 23 (8), 954-966.

Rosa, E. M., & Tudge, J. (2013). Urie Bronfenbrenner’s theory of human development: Its evolution from ecology to bioecology.  Journal of family theory & review ,  5 (4), 243-258.

Rhodes, S. (2013).  Bronfenbrenner’s Ecological Theory  [PDF]. Retrieved from http://uoit.blackboard.com

Tudge, J. R., Mokrova, I., Hatfield, B. E., & Karnik, R. B. (2009). Uses and misuses of Bronfenbrenner’s bioecological theory of human development.  Journal of family theory & review ,  1 (4), 198-210.

Wilson, P., Atkinson, M., Hornby, G., Thompson, M., Cooper, M., Hooper, C. M., & Southall, A. (2002). Young minds in our schools-a guide for teachers and others working in schools . Year: YoungMinds (Jan 2004).

Further Information

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood. Child Development, 45.

• Business Essentials
• Leadership & Management
• Credential of Leadership, Impact, and Management in Business (CLIMB)
• Entrepreneurship & Innovation
• Digital Transformation
• Finance & Accounting
• Business in Society
• For Organizations
• Support Portal
• Media Coverage
• Founding Donors
• Leadership Team

• Harvard Business School →
• HBS Online →
• Business Insights →

Business Insights

Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.

• Career Development
• Communication
• Decision-Making
• Earning Your MBA
• Negotiation
• News & Events
• Productivity
• Staff Spotlight
• Student Profiles
• Work-Life Balance
• AI Essentials for Business
• Alternative Investments
• Business Analytics
• Business Strategy
• Business and Climate Change
• Creating Brand Value
• Design Thinking and Innovation
• Digital Marketing Strategy
• Disruptive Strategy
• Economics for Managers
• Entrepreneurship Essentials
• Financial Accounting
• Global Business
• Launching Tech Ventures
• Leadership Principles
• Leadership, Ethics, and Corporate Accountability
• Leading Change and Organizational Renewal
• Leading with Finance
• Management Essentials
• Negotiation Mastery
• Organizational Leadership
• Power and Influence for Positive Impact
• Strategy Execution
• Sustainable Business Strategy
• Sustainable Investing
• Winning with Digital Platforms

A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

Access your free e-book today.

What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

About the Author

• Skip to content
• Jump to navigation

Page Outlines

IE10 and below are not supported.

Contact us for any help on browser support

• Living in Ottawa
• Recreation and parks
• Garbage and recycling
• Health and public safety
• Parking, roads, and travel
• Jobs with the City
• Planning, development and construction
• Family and social services
• Arts, heritage and events

Some content on this page may not display correctly. Please enable JavaScript in your browser's settings and refresh the page.

New! Apply online for all Building Code Services permits and applications!

City residents, builders and the development industry may now submit and pay for all Building Code Services permits and applications from the comfort of their home, office or work site using any mobile device. The new Building, Planning and Land Development online customer portal is available through My Service Ottawa.

Building Code Services online applications and permits introduces a new paperless process that allows you, the applicant, to apply, upload plans and documents, submit payment, book inspections, and check the application status without traveling to your nearest Client Service Center.

Building Code Services available online include:

• Access to Building Permit Records
• Compliance Reporting
• Building Permits and related applications
• Building Enforcement, Prosecutions, and Inspections
• Municipal Addressing and Permanent Sign Permits
• Request for Service (RFS) - Building Code
• Applications for 911 Blade Signs
• Municipal Notification Agency and Letter of Approval and for the Alcohol and Gaming Commission of Ontario (AGCO)

Building Code Services Client Service Centers continue to be available to client’s requiring assistance.

Getting Started

Visit Planning your project | City of Ottawa to learn more about planning your construction project.

Visit Building permit approval process | City of Ottawa to learn more about applying online for Building Code Services permits and applications.

• Other City Websites
• Privacy Statement
• Accessibility
• Terms of Use
• Technical Support
• powered by EngagementHQ
• Facebook (link is external)
• YouTube (link is external)
• Twitter (link is external)
• Instagram (link is external)

Send us your feedback. We would love to hear from you!

© 2001 - 2019 City of Ottawa

• International
• Education Jobs
• Schools directory
• Resources Education Jobs Schools directory News Search

Edexcel GCSE Psychology: PIAGET'S THEORY OF COGNITIVE DEVELOPMENT & INTELLIGENCE [Development Topic]

Subject: Psychology

Age range: 14-16

Resource type: Lesson (complete)

Last updated

9 September 2024

• Share through email
• Share through twitter
• Share through linkedin
• Share through facebook
• Share through pinterest

This lesson was created using the Edexcel GCSE Specification although content and activities may be useful for other specifications.

Key content covered in this Lesson:

• Lesson objectives
• Piaget’s Explanation of Understanding The World
• Schemas/Schemata
• Disequilibrium and Equilibrium
• How Learning Takes Place: Assimilation and Accommodation
• Activity: Summary Table of Piaget’s Theory of Cognitive Development
• Activity: Apply it - Schemas at The Zoo
• Evaluation worksheet
• Evaluation points
• Exam Practice with Mark Scheme: Application, Evaluation Questions
• Plenary: Consolidation Question

To request lessons, provide feedback or if you have had any issues opening any resources of my resources, please feel free to contact [email protected] .

Reviews and feedback are always welcome.

Tes paid licence How can I reuse this?

Your rating is required to reflect your happiness.

It's good to leave some feedback.

Something went wrong, please try again later.

This resource hasn't been reviewed yet

To ensure quality for our reviews, only customers who have purchased this resource can review it

Report this resource to let us know if it violates our terms and conditions. Our customer service team will review your report and will be in touch.

Not quite what you were looking for? Search by keyword to find the right resource:

An official website of the United States government

Here’s how you know

Official websites use A .gov website belongs to an official government organization in the United States.

Secure websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the website. Share sensitive information only on official, secure websites.

The U.S. Department of the Treasury’s Community Development Financial Institutions Fund (CDFI Fund) released a revised Community Development Financial Institution (CDFI) Certification Application Frequently Asked Questions (FAQs) document, Pre-Approved Target Market Assessment Methodology guidance, and an adjusted CDFI Certification Application timeline today. 

The FAQs and guidance supersede those published December 07, 2023, by adding, revising, or updating select questions from the earlier editions. Noteworthy updates cover:

• Inclusion of home equity lines of credit (HELOCs) to the list of exceptions for mortgage loans that satisfy the responsible financing standards.
• Housing Unit Income Restrictions, which allows for an assessment based on income restrictions on any financed housing units.
• Legal Guardianship/Caretaker, which allows for an assessment based on an individual’s status as legal guardian or caretaker of a person with a disability.
• Date change for when an Applicant serving a non-metropolitan Customized Investment Area (CIA) (or non-Metro County or parish Investment Areas) must begin to direct at least 85% of its financing activity within qualified census tracts of the CIA (in order for non-qualifying activity to count towards the 60% Target Market benchmark).
• Timing for when a CDFI must begin abiding by new pre-approved Target Market Assessment Methodologies.
• Types of Information that an Applicant should include to explain why they do not consider a borrower’s ability to pay back a loan for any of its covered mortgage, consumer, or small business loan products.
• Guidance on demonstrating how certain overdraft programs or nonsufficient funds (NSF) fees may still be consistent with a community development mission.
• Information on loan purchases and loan participations.
• Replaced census tract as the comparison geographic area used to determine whether an individual’s income adjusted for family size qualifies as Low-Income impacting the assessment methods for Low-Income Targeted Population (LITP), including the CDFI Fund’s Low-Income Calculator.

Additionally, the CDFI Fund is also publishing the CDFI Certification Agreement template. Successful CDFI Certification Applicants will be required to sign and be subject to the terms and conditions within the Agreement in order to maintain their status as a Certified CDFI.

Please review the FAQs and CDFI Certification Agreement template in the Related Materials section below for a complete explanation of updated guidance information. Complete and updated guidance information can also be found on the CDFI Fund’s website at www.cdfifund.gov/cdficert under Step 2: Apply .

Early Submission Timeline  To accommodate the release of the amended FAQs, the CDFI Fund is making an adjustment to the early reapplication submission window. Organizations that previously notified the CDFI Fund they wished to apply before the general recertification application deadline, will now reapply for CDFI Certification between October 28, 2024, and January 6, 2025 . As previously indicated by the CDFI Fund:

• Early applicants whose applications are declined and therefore are not recertified will remain in a grace period and retain Certification through December 31, 2025 , but must submit a new CDFI Certification Application by December 31, 2025, to remain Certified.
• Early applicants that choose not to apply during the revised early reapplication window must submit their applications according to the general reapplication deadlines outlined in the following General Reapplication Submission Deadlines chart.
• All other currently Certified CDFIs must submit their reapplication in the Awards Management Information System (AMIS) by the general reapplication deadlines. No additional grace periods or extensions will be granted to CDFIs that miss the revised deadline. 

General Reapplication Submission Deadlines All currently Certified CDFIs must reapply for CDFI Certification following the submission schedule provided below, which remains unchanged since it was published on June 20, 2024. Organizations that do not submit an Application by 11:59 pm Eastern Time (ET) on their appointed deadline will lose their CDFI Certification. General Reapplication Submission Deadlines, by FYE Date

Until their submission due date, currently Certified CDFIs will: 1) retain their status as Certified CDFIs; and 2) remain eligible to apply for all CDFI Fund programs where CDFI Certification is an eligibility requirement—provided they abide by Annual Certification and Data Collection Report (ACR) requirements and have no material event affecting their CDFI Certification status. After submitting their revised CDFI Certification Application, CDFIs will retain their Certified status unless notified otherwise by the CDFI Fund.

Questions regarding the:

• CDFI Certification Application  should be directed to the Office of Certification Policy and Evaluation (OCPE) via an AMIS Service Request (preferred), or by email to  [email protected] .
• ACR and Transaction Level Report  should be directed to the Office of Financial Strategies and Research (FS&R), via an AMIS Service Request (preferred), or by email to  [email protected] .

Related Materials

• Frequently Asked Questions: CDFI Certification Application and Related Tools   (Updated September 6, 2024)
• CDFI Certification Agreement Template (Released September 6, 2024)
• Pre-Approved Target Market Assessment Methodologies  (Updated September 6, 2024)
• Quick Facts for Currently Certified CDFIs  (Updated September 6, 2024)

All CDFI Certification Application guidance materials and resources can be found on the CDFI Fund’s website at www.cdfifund.gov/cdficert under ‘ Step 2:Application Process ’. 

Stay in Touch

Sign up to receive news and important information.

• Become a Member
•   Member Login

Home » News » Practical Application of Toxicology in Drug Development

Practical Application of Toxicology in Drug Development

The highly acclaimed Practical Application of Toxicology in Drug Development (PATDD) course co-organised by Cambridge Academy of Therapeutic Sciences (CATS) and The British Toxicology Society (BTS) provides comprehensive training in toxicology as applied to drug development, catering to scientists from around the globe. Participants will gain a thorough understanding of non-clinical safety evaluation principles, with a strong focus on practical applications and interpreting safety data. Both European and US regulatory toxicology perspectives will be covered.

Who Should Attend?

This course is ideal for professionals from:

• Biotechnology and Pharmaceutical Companies
• Contract Research Organizations (CROs) working with both small and large molecules
• Regulatory Agencies and Academia with an interest in toxicology and drug safety assessment
• Early Career Scientists seeking advanced knowledge in toxicology
• Scientists in Related Fields (e.g., chemistry, biochemistry, molecular biology, medicine) looking to transition into drug safety assessment

Course Highlights

• Virtual Interactive Case Studies and Breakout Groups
• Live Q&A Sessions with Industry Experts
• Unlimited On-Demand Access to Recorded Lectures and Course Materials

Course Details

• Dates: 7th October, 14th October, 28th October, 4th November and 12th November 2024.
• Q&A Sessions on the above dates
• Case Studies: 12th November
• Registrations Open Now: Group discounts, bursary and BTS/ACT discounts available!

Find out more here

Member Login

Privacy overview.

Physical Review Letters

• Collections
• Editorial Team

Negative Conductivity Induced Reconfigurable Gain Metasurfaces and Their Nonlinearity

Xiaoyue zhu, chao qian, erping li, and hongsheng chen, phys. rev. lett. 133 , 113801 – published 9 september 2024.

• No Citing Articles
• Supplemental Material
• Theoretical analysis—
• Gain metasurface designs and…
• Experimental demonstrations—
• Nonlinearity for wave-based neural…
• Discussion—
• ACKNOWLEDGMENTS

The past decades have witnessed the rapid development of metamaterials and metasurfaces. However, loss is still a challenging problem limiting numerous practical applications, including long-range wireless communications, superscattering, and non-Hermitian physics. Recently, great effort has been made to minimize the loss, however, they are too complicated for practical implementation and still restricted by the theoretical limit. Here, we propose and experimentally realize a tunable gain metasurface induced by negative conductivity, with deep theoretical analysis from scattering theory and equivalent circuits. In the experiment, we create metasurface samples embedded with tunable negative (or positive) conductivity to achieve adjustable gain (or loss). By varying the control bias voltages, the metasurfaces can reflect incident waves with additional controllable gain. Interestingly, we find the gain metasurfaces inherently pose nonlinearities, which are beneficial for nonlinear optics and microwave applications, particularly for the nonlinear activation of wave-based neural networks.

• Received 22 January 2024
• Revised 30 May 2024
• Accepted 24 July 2024

DOI: https://doi.org/10.1103/PhysRevLett.133.113801

© 2024 American Physical Society

Physics Subject Headings (PhySH)

• Research Areas

Authors & Affiliations

• 1 ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University , Hangzhou 310027, China
• 2 ZJU-Hangzhou Global Science and Technology Innovation Center, Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Zhejiang University , Hangzhou 310027, China
• 3 Jinhua Institute of Zhejiang University, Zhejiang University , Jinhua 321099, China
• * Contact author: [email protected]
• † Contact author: [email protected]

Article Text (Subscription Required)

Supplemental material (subscription required), references (subscription required).

Vol. 133, Iss. 11 — 13 September 2024

Access Options

• Buy Article »
• Log in with individual APS Journal Account »
• Log in with a username/password provided by your institution »
• Get access through a U.S. public or high school library »

Authorization Required

Other options.

• Buy Article »
• Find an Institution with the Article »

Download & Share

Conceptual illustration of gain metasurfaces. When the incident wave impinges on the metasurface, it is reflected with an additional gain that can be modulated by adjusting the bias voltages ( V 1 − V 4 ). The coordinate is settled at the center of metasurfaces. The diagram at the center bottom represents the operational states of the metamaterials. Typically, metasurfaces used to work in a lossy state represented by the purple region where magnitudes of reflection coefficients ( Γ ) are less than 1. The bottom left corner of the figure elucidates the underlying microscopic mechanisms, where conductivity [ σ ( ω ) ] and imaginary components of the effective permittivity [ ε eff ″ ( ω ) ] are positive. In contrast, ours operate in gain states denoted by the orange region where magnitudes of Γ exceed 1. To achieve this, ε eff ″ ( ω ) and σ ( ω ) should be negative, which also leads to changes in currents as illustrated by J ¯ 2 and J ¯ 1 . S ¯ in and S ¯ out refer to Poynting vectors of incident and reflected waves. And S ¯ ′ indicates the Poynting vector of additional power flow provided by the gain metasurfaces where Re [ ∇ · S ¯ ] > 0 .

Design of gain metasurfaces. (a) Three-dimensional illustration of the gain metasurface. The unit cell structure can be characterized by the structural parameters: a 1 = 50 , b 1 = 60 , h = 1 , w 2 = 10 , w 1 = 19 , p 1 = 48 , p 2 = 20 , g 1 = 2 (units: mm). The overall 3D size is a 1 · b 1 · h 1 . TD indicates the tunnel diode. The symbols Z 21 , Z 22 , Z 23 denote equivalent impedances of these three metallic patch parts, respectively. (b) Equivalent circuit models. Both Γ L and Z L represent reflection coefficients and the equivalent terminal impedance observed from the reflection wave ports, respectively. − R d can be continuously manipulated in positive or negative regions. (c) Current-voltage ( I − V ) curves. Tested currents with voltages directly varying. The differential resistance values R 1 , R 2 , R 3 , and R 4 are about − 42     Ω , − 17.5     Ω , 0     Ω , and 48     Ω , respectively. (d) Simulated spectra of reflection coefficients with the differential resistances of TD being R 1 ∼ R 4 .

Experimental results. (a) A photograph of experimental setups. T and R represent the transmitter and receiver, respectively. The H -shaped gain metasurfaces can work in a a wide range of incident angle. (b) Gain—voltage curves. Measured gains of the two samples are plotted correspondingly with the total bias voltage changing. The two kinds of samples are designed to work in different bands. More details can be found in Supplemental Material [ 33 ], notes 2 and 3. (c),(d) The gain evolution processes of samples 1 and 2 with total bias voltage changing.

Natural nonlinearity and potential applications in wave-based neural networks. (a) An illustration of wave-based neural network structure, which encompasses matrix multiplication and nonlinear activation. The waves that propagate from one layer to the next carry varying phase and magnitude information, acting as trainable weights between adjacent network layers. Herein, X 1 − X 4 , Y 1 − Y 4 , b , and f are inputs, outputs, bias, and activation, respectively. (b) With the input power linearly enlarging, the output power can increase nonlinearly.

Sign up to receive regular email alerts from Physical Review Letters

• Forgot your username/password?
• Create an account

Article Lookup

Paste a citation or doi, enter a citation.