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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

Prevent plagiarism. Run a free check.

Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Lab Report Format: Step-by-Step Guide & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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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.

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In psychology, a lab report outlines a study’s objectives, methods, results, discussion, and conclusions, ensuring clarity and adherence to APA (or relevant) formatting guidelines.

A typical lab report would include the following sections: title, abstract, introduction, method, results, and discussion.

The title page, abstract, references, and appendices are started on separate pages (subsections from the main body of the report are not). Use double-line spacing of text, font size 12, and include page numbers.

The report should have a thread of arguments linking the prediction in the introduction to the content of the discussion.

This must indicate what the study is about. It must include the variables under investigation. It should not be written as a question.

Title pages should be formatted in APA style .

The abstract provides a concise and comprehensive summary of a research report. Your style should be brief but not use note form. Look at examples in journal articles . It should aim to explain very briefly (about 150 words) the following:

• Start with a one/two sentence summary, providing the aim and rationale for the study.
• Describe participants and setting: who, when, where, how many, and what groups?
• Describe the method: what design, what experimental treatment, what questionnaires, surveys, or tests were used.
• Describe the major findings, including a mention of the statistics used and the significance levels, or simply one sentence summing up the outcome.
• The final sentence(s) outline the study’s “contribution to knowledge” within the literature. What does it all mean? Mention the implications of your findings if appropriate.

The abstract comes at the beginning of your report but is written at the end (as it summarises information from all the other sections of the report).

Introduction

The purpose of the introduction is to explain where your hypothesis comes from (i.e., it should provide a rationale for your research study).

Ideally, the introduction should have a funnel structure: Start broad and then become more specific. The aims should not appear out of thin air; the preceding review of psychological literature should lead logically into the aims and hypotheses.

• Start with general theory, briefly introducing the topic. Define the important key terms.
• Explain the theoretical framework.
• Summarise and synthesize previous studies – What was the purpose? Who were the participants? What did they do? What did they find? What do these results mean? How do the results relate to the theoretical framework?
• Rationale: How does the current study address a gap in the literature? Perhaps it overcomes a limitation of previous research.
• Aims and hypothesis. Write a paragraph explaining what you plan to investigate and make a clear and concise prediction regarding the results you expect to find.

There should be a logical progression of ideas that aids the flow of the report. This means the studies outlined should lead logically to your aims and hypotheses.

Do be concise and selective, and avoid the temptation to include anything in case it is relevant (i.e., don’t write a shopping list of studies).

USE THE FOLLOWING SUBHEADINGS:

Participants

• How many participants were recruited?
• Say how you obtained your sample (e.g., opportunity sample).
• Give relevant demographic details (e.g., gender, ethnicity, age range, mean age, and standard deviation).
• State the experimental design .
• What were the independent and dependent variables ? Make sure the independent variable is labeled and name the different conditions/levels.
• For example, if gender is the independent variable label, then male and female are the levels/conditions/groups.
• How were the IV and DV operationalized?
• Identify any controls used, e.g., counterbalancing and control of extraneous variables.
• List all the materials and measures (e.g., what was the title of the questionnaire? Was it adapted from a study?).
• You do not need to include wholesale replication of materials – instead, include a ‘sensible’ (illustrate) level of detail. For example, give examples of questionnaire items.
• Include the reliability (e.g., alpha values) for the measure(s).
• Describe the precise procedure you followed when conducting your research, i.e., exactly what you did.
• Describe in sufficient detail to allow for replication of findings.
• Be concise in your description and omit extraneous/trivial details, e.g., you don’t need to include details regarding instructions, debrief, record sheets, etc.
• Assume the reader has no knowledge of what you did and ensure that he/she can replicate (i.e., copy) your study exactly by what you write in this section.
• Write in the past tense.
• Don’t justify or explain in the Method (e.g., why you chose a particular sampling method); just report what you did.
• Only give enough detail for someone to replicate the experiment – be concise in your writing.
• The results section of a paper usually presents descriptive statistics followed by inferential statistics.
• Report the means, standard deviations, and 95% confidence intervals (CIs) for each IV level. If you have four to 20 numbers to present, a well-presented table is best, APA style.
• Name the statistical test being used.
• Report appropriate statistics (e.g., t-scores, p values ).
• Report the magnitude (e.g., are the results significant or not?) as well as the direction of the results (e.g., which group performed better?).
• It is optional to report the effect size (this does not appear on the SPSS output).
• Avoid interpreting the results (save this for the discussion).
• Make sure the results are presented clearly and concisely. A table can be used to display descriptive statistics if this makes the data easier to understand.
• DO NOT include any raw data.
• Follow APA style.

Use APA Style

• Numbers reported to 2 d.p. (incl. 0 before the decimal if 1.00, e.g., “0.51”). The exceptions to this rule: Numbers which can never exceed 1.0 (e.g., p -values, r-values): report to 3 d.p. and do not include 0 before the decimal place, e.g., “.001”.
• Percentages and degrees of freedom: report as whole numbers.
• Statistical symbols that are not Greek letters should be italicized (e.g., M , SD , t , X 2 , F , p , d ).
• Include spaces on either side of the equals sign.
• When reporting 95%, CIs (confidence intervals), upper and lower limits are given inside square brackets, e.g., “95% CI [73.37, 102.23]”
• Outline your findings in plain English (avoid statistical jargon) and relate your results to your hypothesis, e.g., is it supported or rejected?
• Compare your results to background materials from the introduction section. Are your results similar or different? Discuss why/why not.
• How confident can we be in the results? Acknowledge limitations, but only if they can explain the result obtained. If the study has found a reliable effect, be very careful suggesting limitations as you are doubting your results. Unless you can think of any c onfounding variable that can explain the results instead of the IV, it would be advisable to leave the section out.
• Suggest constructive ways to improve your study if appropriate.
• What are the implications of your findings? Say what your findings mean for how people behave in the real world.
• Suggest an idea for further research triggered by your study, something in the same area but not simply an improved version of yours. Perhaps you could base this on a limitation of your study.
• Concluding paragraph – Finish with a statement of your findings and the key points of the discussion (e.g., interpretation and implications) in no more than 3 or 4 sentences.

Reference Page

The reference section lists all the sources cited in the essay (alphabetically). It is not a bibliography (a list of the books you used).

In simple terms, every time you refer to a psychologist’s name (and date), you need to reference the original source of information.

If you have been using textbooks this is easy as the references are usually at the back of the book and you can just copy them down. If you have been using websites then you may have a problem as they might not provide a reference section for you to copy.

References need to be set out APA style :

Author, A. A. (year). Title of work . Location: Publisher.

Journal Articles

Author, A. A., Author, B. B., & Author, C. C. (year). Article title. Journal Title, volume number (issue number), page numbers

A simple way to write your reference section is to use Google scholar . Just type the name and date of the psychologist in the search box and click on the “cite” link.

Next, copy and paste the APA reference into the reference section of your essay.

Once again, remember that references need to be in alphabetical order according to surname.

Psychology Lab Report Example

Quantitative paper template.

Quantitative professional paper template: Adapted from “Fake News, Fast and Slow: Deliberation Reduces Belief in False (but Not True) News Headlines,” by B. Bago, D. G. Rand, and G. Pennycook, 2020,  Journal of Experimental Psychology: General ,  149 (8), pp. 1608–1613 ( https://doi.org/10.1037/xge0000729 ). Copyright 2020 by the American Psychological Association.

Qualitative paper template

Qualitative professional paper template: Adapted from “‘My Smartphone Is an Extension of Myself’: A Holistic Qualitative Exploration of the Impact of Using a Smartphone,” by L. J. Harkin and D. Kuss, 2020,  Psychology of Popular Media ,  10 (1), pp. 28–38 ( https://doi.org/10.1037/ppm0000278 ). Copyright 2020 by the American Psychological Association.

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• Knowledge Base
• Methodology
• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

Prevent plagiarism, run a free check.

Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

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Bevans, R. (2022, December 05). A Quick Guide to Experimental Design | 5 Steps & Examples. Scribbr. Retrieved 12 August 2024, from https://www.scribbr.co.uk/research-methods/guide-to-experimental-design/

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• Scientific Lab Reports
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Writing a Lab Report

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Writing a scientific lab report is significantly different from writing for other classes like philosophy, English, and history. The most prominent form of writing in biology, chemistry, and environmental science is the lab report, which is a formally written description of results and discoveries found in an experiment. College lab reports should emulate and follow the same formats as reports found in scholarly journals, such as Nature , Cell , and The American Journal of Biochemistry .

Report Format

Title: The title says what you did. It should be brief (aim for ten words or less) and describe the main point of the experiment or investigation.

• Example:  Caffeine Increases Amylase Activity in the Mealworm ( Tenebrio molitar).
• If you can, begin your title using a keyword rather than an article like “The” or “A.”

Abstract: An abstract is a very concise summary of the purpose of the report, data presented, and major conclusions in about 100 - 200 words.  Abstracts are also commonly required for conference/presentation submissions because they summarize all of the essential materials necessary to understand the purpose of the experiment. They should consist of a background sentence , an introduction sentence , your hypothesis/purpose of the experiment, and a sentence about the results and what this means.

Introduction: The introduction of a lab report defines the subject of the report, provides background information and relevant studies, and outlines scientific purpose(s) and/or objective(s).

• The introduction is a place to provide the reader with necessary research on the topic and properly cite sources used.
• Summarizes the current literature on the topic including primary and secondary sources.
• Introduces the paper’s aims and scope.
• States the purpose of the experiment and the hypothesis.

Materials and Methods: The materials and methods section is a vital component of any formal lab report. This section of the report gives a detailed account of the procedure that was followed in completing the experiment as well as all important materials used. (This includes bacterial strains and species names in tests using living subjects.)

• Discusses the procedure of the experiment in as much detail as possible.
• Provides information about participants, apparatus, tools, substances, location of experiment, etc.
• For field studies, be sure to clearly explain where and when the work was done.
• It must be written so that anyone can use the methods section as instructions for exact replications.
• Don’t hesitate to use subheadings to organize these categories.
• Practice proper scientific writing forms. Be sure to use the proper abbreviations for units. Example: The 50mL sample was placed in a 5ºC room for 48hrs.

Results: The results section focuses on the findings, or data, in the experiment, as well as any statistical tests used to determine their significance.

• Concentrate on general trends and differences and not on trivial details.
• Summarize the data from the experiments without discussing their implications (This is where all the statistical analyses goes.)
• Organize data into tables, figures, graphs, photographs, etc.  Data in a table should not be duplicated in a graph or figure. Be sure to refer to tables and graphs in the written portion, for example, “Figure 1 shows that the activity....”
• Number and title all figures and tables separately, for example, Figure 1 and Table 1 and include a legend explaining symbols and abbreviations. Figures and graphs are labeled below the image while tables are labeled above.

  Discussion: The discussion section interprets the results, tying them back to background information and experiments performed by others in the past.This is also the area where further research opportunities shold be explored.

• Interpret the data; do not restate the results.
• Observations should also be noted in this section, especially anything unusual which may affect your results.

For example, if your bacteria was incubated at the wrong temperature or a piece of equipment failed mid-experiment, these should be noted in the results section.

• Relate results to existing theories and knowledge.This can tie back to your introduction section because of the background you provided.
• Explain the logic that allows you to accept or reject your original hypotheses.
• Include suggestions for improving your techniques or design, or clarify areas of doubt for further research.

Acknowledgements and References: A references list should be compiled at the end of the report citing any works that were used to support the paper. Additionally, an acknowledgements section should be included to acknowledge research advisors/ partners, any group or person providing funding for the research and anyone outside the authors who contributed to the paper or research.

General Tips

• In scientific papers, passive voice is perfectly acceptable. On the other hand, using “I” or “we” is not.

          Incorrect: We found that caffeine increased amylase levels in Tenebrio molitar.  Correct: It was discovered that caffeine increased amylase levels in Tenebrio molitar.   

• It is expected that you use as much formal (bland) language and scientific terminology as you can. There should be no emphasis placed on “expressing yourself” or “keeping it interesting”; a lab report is not a narrative.
• In a lab report, it is important to get to the point. Be descriptive enough that your audience can understand the experiment, but strive to be concise.
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Writing an Introduction for a Scientific Paper

Dr. michelle harris, dr. janet batzli, biocore.

This section provides guidelines on how to construct a solid introduction to a scientific paper including background information, study question , biological rationale, hypothesis , and general approach . If the Introduction is done well, there should be no question in the reader’s mind why and on what basis you have posed a specific hypothesis.

Broad Question : based on an initial observation (e.g., “I see a lot of guppies close to the shore. Do guppies like living in shallow water?”). This observation of the natural world may inspire you to investigate background literature or your observation could be based on previous research by others or your own pilot study. Broad questions are not always included in your written text, but are essential for establishing the direction of your research.

Background Information : key issues, concepts, terminology, and definitions needed to understand the biological rationale for the experiment. It often includes a summary of findings from previous, relevant studies. Remember to cite references, be concise, and only include relevant information given your audience and your experimental design. Concisely summarized background information leads to the identification of specific scientific knowledge gaps that still exist. (e.g., “No studies to date have examined whether guppies do indeed spend more time in shallow water.”)

Testable Question : these questions are much more focused than the initial broad question, are specific to the knowledge gap identified, and can be addressed with data. (e.g., “Do guppies spend different amounts of time in water <1 meter deep as compared to their time in water that is >1 meter deep?”)

Biological Rationale : describes the purpose of your experiment distilling what is known and what is not known that defines the knowledge gap that you are addressing. The “BR” provides the logic for your hypothesis and experimental approach, describing the biological mechanism and assumptions that explain why your hypothesis should be true.

The biological rationale is based on your interpretation of the scientific literature, your personal observations, and the underlying assumptions you are making about how you think the system works. If you have written your biological rationale, your reader should see your hypothesis in your introduction section and say to themselves, “Of course, this hypothesis seems very logical based on the rationale presented.”

• A thorough rationale defines your assumptions about the system that have not been revealed in scientific literature or from previous systematic observation. These assumptions drive the direction of your specific hypothesis or general predictions.
• Defining the rationale is probably the most critical task for a writer, as it tells your reader why your research is biologically meaningful. It may help to think about the rationale as an answer to the questions— how is this investigation related to what we know, what assumptions am I making about what we don’t yet know, AND how will this experiment add to our knowledge? *There may or may not be broader implications for your study; be careful not to overstate these (see note on social justifications below).
• Expect to spend time and mental effort on this. You may have to do considerable digging into the scientific literature to define how your experiment fits into what is already known and why it is relevant to pursue.
• Be open to the possibility that as you work with and think about your data, you may develop a deeper, more accurate understanding of the experimental system. You may find the original rationale needs to be revised to reflect your new, more sophisticated understanding.
• As you progress through Biocore and upper level biology courses, your rationale should become more focused and matched with the level of study e ., cellular, biochemical, or physiological mechanisms that underlie the rationale. Achieving this type of understanding takes effort, but it will lead to better communication of your science.

***Special note on avoiding social justifications: You should not overemphasize the relevance of your experiment and the possible connections to large-scale processes. Be realistic and logical —do not overgeneralize or state grand implications that are not sensible given the structure of your experimental system. Not all science is easily applied to improving the human condition. Performing an investigation just for the sake of adding to our scientific knowledge (“pure or basic science”) is just as important as applied science. In fact, basic science often provides the foundation for applied studies.

Hypothesis / Predictions : specific prediction(s) that you will test during your experiment. For manipulative experiments, the hypothesis should include the independent variable (what you manipulate), the dependent variable(s) (what you measure), the organism or system , the direction of your results, and comparison to be made.

If you are doing a systematic observation , your hypothesis presents a variable or set of variables that you predict are important for helping you characterize the system as a whole, or predict differences between components/areas of the system that help you explain how the system functions or changes over time.

Experimental Approach : Briefly gives the reader a general sense of the experiment, the type of data it will yield, and the kind of conclusions you expect to obtain from the data. Do not confuse the experimental approach with the experimental protocol . The experimental protocol consists of the detailed step-by-step procedures and techniques used during the experiment that are to be reported in the Methods and Materials section.

Some Final Tips on Writing an Introduction

• As you progress through the Biocore sequence, for instance, from organismal level of Biocore 301/302 to the cellular level in Biocore 303/304, we expect the contents of your “Introduction” paragraphs to reflect the level of your coursework and previous writing experience. For example, in Biocore 304 (Cell Biology Lab) biological rationale should draw upon assumptions we are making about cellular and biochemical processes.
• Be Concise yet Specific: Remember to be concise and only include relevant information given your audience and your experimental design. As you write, keep asking, “Is this necessary information or is this irrelevant detail?” For example, if you are writing a paper claiming that a certain compound is a competitive inhibitor to the enzyme alkaline phosphatase and acts by binding to the active site, you need to explain (briefly) Michaelis-Menton kinetics and the meaning and significance of Km and Vmax. This explanation is not necessary if you are reporting the dependence of enzyme activity on pH because you do not need to measure Km and Vmax to get an estimate of enzyme activity.
• Another example: if you are writing a paper reporting an increase in Daphnia magna heart rate upon exposure to caffeine you need not describe the reproductive cycle of magna unless it is germane to your results and discussion. Be specific and concrete, especially when making introductory or summary statements.

Where Do You Discuss Pilot Studies? Many times it is important to do pilot studies to help you get familiar with your experimental system or to improve your experimental design. If your pilot study influences your biological rationale or hypothesis, you need to describe it in your Introduction. If your pilot study simply informs the logistics or techniques, but does not influence your rationale, then the description of your pilot study belongs in the Materials and Methods section.  

How will introductions be evaluated? The following is part of the rubric we will be using to evaluate your papers.

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• Aims and Hypotheses

There is no research without a proper aim and hypotheses – aims and hypotheses in research are the supporting frameworks on a path to new scientific discoveries. To better understand their importance, let us first analyse the difference between aims and hypotheses in psychology , examine their purpose, and give some examples.

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What is the purpose of research aims?

How are hypotheses different from aims?

What are the different types of hypotheses?

What type of hypothesis is the following statement: ‘There will be a difference between Mini-Mental Status Examination scores in students who were and were not sleep-deprived?  

What type of hypothesis is the following statement: ‘There will be no difference in time recorded sleeping between students who received good and poor grades in their school report’?

What type of hypothesis is this statement ‘Students who were not sleep-deprived will have higher scores in the Mini-Mental Status Examination test than sleep-deprived students?

What happens when the research paper does not cover all the elements of aims and hypotheses?

What are the components the hypotheses have to include? 

What do operationalising variables mean?

What is a null hypothesis?

What is an alternative directional hypothesis? 

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First, we will define the aims and hypotheses and learn the difference between aims and hypotheses in psychology .

Then, we will look at different types of hypotheses.

Next, we will look at the function of aims and hypotheses in research and psychology.

Later, we will look at specific aims and hypotheses in research examples.

Finally, we will discuss the need and how explicit research aims objectives and hypotheses are implemented.

Difference Between Aims and Hypotheses: Psychology

When you write a research report, you should state the aim first and then the hypothesis.

• The aim is a summary of the goal or purpose of the research.

The aim is a broad starting point that gets narrowed down into the hypothesis.

• The hypothesis is a predictive, testable statement about what the researcher expects to find in the study.

Types of Hypotheses

Before we get into the types of hypotheses, let's quickly recap the hypotheses' components.

The independent variable (IV) is the factor that the researcher manipulates/ changes (this can be naturally occurring in some instances) and is theorised to be the cause of a phenomenon.

And the dependent variable (DV) is the factor that the researcher measures because they believe that the changes in the IV will affect the DV.

There are two types of hypotheses: null and alternative hypotheses.

A null hypothesis states that the independent variable does not influence the dependent variable. The null hypothesis states that changes/ manipulating the IV will not affect the DV.

Research scenario: Investigation of how test results affect sleep.

An example of a null hypothesis is there is no difference in recorded sleep time (dependent variable) between students who received good and poor grades (independent variable).

An alternative hypothesis states that the independent variable has an effect on the dependent variable. Often, it is the same (or very similar) to your research hypothesis.

Research scenario: Investigating how sleep deprivation affects performance on cognitive tests.

An alternative hypothesis may be that the less sleep students get (independent variable), the worse their performance will be on cognitive tests (dependent variable). Not sleep-deprived students will perform better on the Mini-Mental Status Examination test than sleep-deprived students.

The alternative hypothesis can be further sub-categorised into a one- or two-tailed hypothesis. A one-tailed hypothesis (also known as a directional hypothesis) suggests that the results can go one way, e.g. it may increase or decrease. And a two-tailed (also known as a non-directional hypothesis) is exactly the opposite; there are two ways the results could expectedly go.

An example of a two-tailed hypothesis is if you flip a coin, you could predict that it will land on either heads or tails.

Aims and Hypothesis in Research

In research, aims and hypotheses play major roles. They are the part of the research that sets you up for the rest of the study. Without strong research aims and hypotheses, your research will lack direction.

First, let's go over the function of research aims.

Research aims provide an overview of the research objective; this allows all researchers to be on the same page about the purpose of the research. Aims also describe why the research is needed and how it complements existing research in the field.

Duplicating research can sometimes be useful, but most times, researchers want to conduct their own new research.

Outside of the researchers, readers can then identify the research topic and whether it interests them.

The research aimed to examine the effects of sleep deprivation on test performance.

But what information do hypotheses provide?

Hypotheses identify the variables studied in an experiment. They describe expected results in terms of the effect of the independent variable on the dependent variable. When readers see the hypothesis, they should know exactly what the researcher expected in the study's outcome (remember, sometimes the researcher can be wrong).

The hypothesis was that the less sleep a student gets (independent variable), the worse grades a student will achieve (dependent variable).

Typically, researchers use hypotheses for statistical tests such as hypothesis testing, which allows them to determine if the original predictions are correct. Hypotheses are helpful because the reader can quickly identify the variables , the expected results based on previous research, and how the experiment should measure these variables .

Hypotheses usually influence the research design and analysis used in conducting the research.

Psychological research must meet a standard for the psychological research community to accept it.

Components of Hypotheses

When writing research hypotheses, there are several essential things to consider, including:

The hypotheses must be clear and concise;

it must be easy to understand and not contain irrelevant details.

The researcher must predict what they expect to find based on reading previous research findings.

The researcher must explain how they arrived at their predictions, citing evidence from prior research.

The researcher must identify all variables they will study.

One study examined how sleep deprivation affects performance on cognitive tests. The hypothesis was to identify sleeping time as the independent variable and cognitive test scores as the dependent variable.

Additionally, the research must operationalise the hypotheses and describe how the variables will be measured.

When assessing cognitive abilities, the researcher should indicate how they will assess the cognitive skills. They could do so with a cognitive test, such as the Mini-Mental Status Examination scores.

Example Hypothesis

A hypothesis denotes a relationship between two variables, the independent and dependent variables. An example hypothesis is the more you sleep, the less tired you will feel.

Aims and Hypotheses: Psychology

Now that we understand the difference between aims and hypotheses, let's take a closer look at their function.

In psychology, aims and hypotheses function very similarly to other research fields. They set up the purpose of a study so that the researchers and readers understand its goals.

The aims establish the reasoning behind the study and why that specific topic is being researched. And the hypotheses share the researchers' expectations. It outlines what the researchers expect when the IV is manipulated.

Studies with well-defined aims and hypotheses allow the research to be more accessible. This means that a professional psychologist, a psychology student, or even someone who is simply curious about the topic can all read the research and understand its purpose.

Aims and Hypotheses in Research Example

As we have learned, aims and hypotheses are crucial in setting up successful research. They exist within every study and help outline the goals and outcomes the researchers expect. To further understand the aims and hypotheses in psychological research, let's look at a famous study – Asch's line experiment .

Solomon Asch conducted a study in 1951 about conformity . This study has become renowned for exposing the strong effects of conformity in a group setting. Asch put one participant in a room with seven strangers, people he said were other participants but were, in fact, confederates.

Confederates are hired actors who are told what to do in the experiment by the researcher.

The participants were tasked with trying to match one line to three other lines. Initially, the confederates would answer correctly, but as the trials continued, they all answered incorrectly. Would the participant still give the correct answer, or would they be swayed by conformity and be wrong?

Asch found that 74% of participants conformed at least once, even though they were obviously giving the wrong answer.

In this experiment, the aim was to look at the effects of conformity. More specifically, Asch aimed to see how impactful groups' pressures are on an individual's conformity. Asch hypothesised that participants would conform to the group when the confederates answered incorrectly due to social pressure.

Since we know the study's outcome, we know that Asch stayed true to his aims and provided supporting evidence for his hypothesis.

Explicit Research Aims, Objectives, and Hypotheses

An explicit research necessity across all disciplines is the operalisation of variables. When talking about operationalising a variable or hypothesis, it means that the term is defined so clearly and succinctly that there is no confusion or any grey area concerning what it means.

When operationally defining variables, researchers need to not only define what the variable is but also how they will measure it. Operationally defined hypotheses not only include detailed descriptions of variables and the outcome but also the relationship between the variables.

Remember, when studies and their results are replicated, they increase in reliability. Researchers operationally defining variables and hypotheses help future researchers replicate their study without confusion. If you do not operationally define key terms of your research and no one can replicate it, is there even a purpose to doing the research at all?

While operationally defining variables and hypotheses might seem like a simple task, it is extremely important for a successful outcome.

Aims and Hypotheses - Key takeaways

• For the scientific psychological community to accept the aim, the objective must explain why the research is needed and how it will expand our current knowledge.
• The two types of hypotheses are null hypothesis and alternative hypothesis.
• For the scientific community of psychologists to accept a hypothesis, it must identify all variables, which researchers must operationalise.

Flashcards in Aims and Hypotheses 28

The purpose of research aims are: 

• provide a summary of what the research goal is 
• describe why the research is needed and how it adds to existing research in the field
• so that the readers can identify what the research topic is and of interest to them

Hypotheses differ from aims because they are statements of the goals and purposes of the research. In contrast, hypotheses are predictive statements concerning expected results. 

The types of hypotheses are:

• Null hypothesis.
• Alternative hypothesis.
• Directional alternative hypothesis (one-tailed) or non-directional (two-tailed).

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Frequently Asked Questions about Aims and Hypotheses

How to write aims and hypotheses?

When writing aims, researchers should summarise the research goal and purpose in a straightforward statement. Moreover, researchers must ensure that it is a predictive and testable statement when writing a hypothesis. This process should summarise the expected results of the study. 

What comes first, hypothesis or aims?

Researchers should write the aims first and then the hypothesis when writing research. 

What are the three types of hypotheses?

The three types of hypotheses are:

What is an aim in psychology?

An aim in psychology is a summary statement of the research's goal or purpose.

How are hypotheses different from aims and objectives?

Hypotheses differ from aims and objectives because aims are a general statement of the research's goals and purposes. In contrast, hypotheses explain precisely the predicted findings in terms of the independent and dependent variables. 

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How to Write up a Science Experiment

Last Updated: May 19, 2023 Fact Checked

This article was co-authored by Jessie Antonellis-John . Jessie Antonellis-John is a Math and Science Instructor who teaches at Southwestern Oregon Community College. With over 10 years of experience, she specializes in curriculum development. Jessie earned her PhD in Teaching & Teacher Education from the University of Arizona, her Master of Education from Western Governors University, and her BS in Astrophysics from Mount Holyoke College. She’s also co-authored several peer-reviewed journal articles in professional publications. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 275,818 times.

Any time you have conducted a science experiment, you should write a lab report detailing why the experiment was performed, the results you expected, the process you used, the actual results, and a discussion of what the results mean. Lab reports often follow a very standard format starting with an abstract and introduction, followed by a materials and methods section, the results and discussion, and finally a conclusion. This format will allow the reader to find answers to common questions that are often asked: Why was the experiment performed? What were the expected results? How was the experiment conducted? What happened in the experiment? What do the results mean? This article explains the basic format of a lab report.

Lab Report Template

Writing an Abstract and Introduction

• The purpose of this short summary is to provide the reader with enough information on the experiment that they can see if they want or need to read the entire report. The abstract helps them determine if your research is relevant to them.
• Devote a sentence to describing the purpose of the project and its significance. Then, very briefly describe the materials and methods used. Follow up with a 1-2 sentence description of the results of the experiment. You might also provide a list of keywords listing subjects related to your research.

• The introduction will outline what the experiment is, why it was done, and why it is important. It must provide the reader with two key pieces of information: what is the question the experiment is supposed to answer and why is answering this question important.

• A research hypothesis should be a brief statement that pares down your problem that you described in your introduction into something that is testable and falsifiable.
• Scientists must create a hypothesis from which an experiment can reasonably be designed and carried out.
• A hypothesis is never proved in an experiment, only "verified" or "supported".

• For example, you might start with "Fertilizer affects how tall a plant will grow". You could expand this idea to a clear hypothesis: "Plants grow faster and taller when they are given fertilizer". To make it a testable hypothesis, you could add experimental details: "Plants which are given a solution with 1ml of fertilizer grow faster than plants without fertilizer because they are given more nutrients."

Explaining Your Research Procedure

• This section is extremely crucial documentation of your methods of analysis.

• For example, if you were testing how fertilizer affects plant growth, you would want to state what brand of fertilizer you used, what species of plant you used and what brand of seed.
• Make sure you include the quantity of all objects used in the experiment.

• Remember all experiments involve controls and variables. Describe these here.
• If you used a published laboratory method, be sure to provide a reference for the original method.

Reporting Results

• For example, if you are testing the effect of fertilizer on plant growth you would want a graph showing the average growth of plants given fertilizer vs. those without.
• You would also want to describe the result. For example "Plants which were given a concentration of 1ml of fertilizer grew an average of 4 cm taller than those that were not given fertilizer."
• As you go along, narrate your results. Tell the reader why a result is significant to the experiment or problem. This will allow the reader to follow your thinking process.
• Compare your results to your original hypothesis. State whether or not your hypothesis was supported or not by your experiment.
• Quantitative data is anything expressed in terms of numerical forms such as percentages or statistics. Qualitative data is derived from broad questions and is expressed in the form of word responses from study participants.

• In this section, the author can address other questions such as: "why did we get an unexpected result?" or "what would happen if one aspect of the procedure was altered?".
• If your results did not verify your hypothesis, explain your reasoning why.

• Be sure to link back to the introduction and whether or not the experiment addressed the goals of your analysis.

• You can use software such as EndNote to help you cite and build a properly referenced bibliography.

Expert Q&A

You Might Also Like

• ↑ Michael Simpson, PhD. Registered Professional Biologist. Expert Interview. 8 September 2021.
• ↑ https://www.matrix.edu.au/how-to-write-a-scientific-report/
• ↑ https://explorable.com/research-hypothesis
• ↑ https://www.monash.edu/learnhq/write-like-a-pro/annotated-assessment-samples/science/science-lab-report

About This Article

When you’re writing up a science experiment for a class, break it into sections for your introduction, procedure, findings, and conclusion. In the intro, explain the purpose of your experiment and what you predicted would happen, then give a brief overview of what you did. In the procedure section, describe all of the materials you used and give a step-by-step account of your method. In the findings section, give the results from your experiment, including any graphs or diagrams you made. Then, explain if your expectations were met and what further research you can do. Finish with a brief conclusion that summarizes your experiment and its results. For more tips from our Science co-author, including how to write an abstract for your science paper, read on! Did this summary help you? Yes No

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Writing Specific Aims

Identifying specific aims.

• Identify a research gap. Can your research move your field forward?
• Determine the significance of the problem and impact. Is the work important—will progress make a difference to our understanding of neuroscience and/or human health?
• Is your team experienced and able to carry out the work?

Outlining Specific Aims

Step 1:  Determine whether your research questions are exploratory (hypothesis-generating) or confirmatory (hypothesis-testing). If confirmatory, make sure the hypotheses are focused, testable, built on a solid scientific foundation, and important.

Step 2: Draft aims to generate and/or test the hypotheses feasibly within the grant period.

• Usually a one-page limit.
• The aims should be focused and easy to assess by reviewers.
• For many mechanisms, consider avoiding interdependent aims.
• Outline experiments and outcomes.
• Determine approximate personnel, resources, and timeline.
• Identify a potential funding institute and funding mechanism.
• Consider potential study sections and expertise of reviewers .  
• Assess feasibility of your proposed work within the proposed funding mechanism.

Step 3: Revise aims as needed.

Writing the Specific Aims

Provide a narrative describing the rationale and significance of your planned research. A good way to start is with a sentence that states your project's goals. In some cases, you may want to explain why you did not take an alternative route. State your hypothesis (if relevant) and briefly describe your aims and how they build on rigorous prior and preliminary studies. If it is likely your application will be reviewed by a study section with broad expertise, summarize the status of research in your field and explain how your project fits in. In the narrative part of the Specific Aims of many applications, people also use their aims to:

State the technologies they plan to use.

Note their expertise to do a specific task or that of collaborators.

Describe past accomplishments related to the project.

Describe preliminary studies and new and highly relevant findings in the field.

Explain their area's biology.

Show how the aims relate to one another.

Use bold or italics to emphasize items they want to bring to the reviewers' attention, such as the hypothesis or rationale.

Depending on your situation, decide which items are important for you. For example, an Early-Stage Investigator may want to highlight preliminary data and qualifications to do the work.

After the narrative, enter your aims as stand-alone headers, run-on headers, or bullet points

State your plans using strong verbs like identify, define, quantify, establish, determine.

Describe each aim in one to three sentences.

Consider adding bullets under each aim to refine your objectives.

Describe expected outcomes for each aim.

Explain how you plan to interpret data from the aim’s efforts.

Describe how to address potential pitfalls with contingency plans.

Some people add a closing paragraph, emphasizing the significance of the work, their collaborators, or whatever else they want to focus reviewers' attention on.

It can be useful to have a colleague review your aims for clarity (particularly a colleague outside your field or a colleague with NIH funding or NIH study section experience). 

Want to contact NINDS staff? Please visit our Find Your NINDS Program Officer page to learn more about contacting Program Officers, Grants Management Specialists, Scientific Review Officers, and Health Program Specialists. Find Your Program Officer

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Jean Hébert, a new hire with the US Advanced Projects Agency for Health ( ARPA-H ), is expected to lead a major new initiative around “functional brain tissue replacement,” the idea of adding youthful tissue to people’s brains. 

President Joe Biden created ARPA-H in 2022, as an agency within the Department of Health and Human Services, to pursue what he called  “bold, urgent innovation” with transformative potential. 

The brain renewal concept could have applications such as treating stroke victims, who lose areas of brain function. But Hébert, a biologist at the Albert Einstein school of medicine, has most often proposed total brain replacement, along with replacing other parts of our anatomy, as the only plausible means of avoiding death from old age.

As he described in his 2020 book, Replacing Aging, Hébert thinks that to live indefinitely people must find a way to substitute all their body parts with young ones, much like a high-mileage car is kept going with new struts and spark plugs.

The idea has a halo of plausibility since there are already liver transplants and titanium hips, artificial corneas and substitute heart valves. The trickiest part is your brain. That ages, too, shrinking dramatically in old age. But you don’t want to swap it out for another—because it is you.

And that’s where Hébert's research comes in. He’s been exploring ways to “progressively” replace a brain by adding bits of youthful tissue made in a lab. The process would have to be done slowly enough, in steps, that your brain could adapt, relocating memories and your self-identity.  

During a visit this spring to his lab at Albert Einstein, Hébert showed MIT Technology Review how he has been carrying out initial experiments with mice, removing small sections of their brains and injecting slurries of embryonic cells. It’s a step toward proving whether such youthful tissue can survive and take over important functions.

To be sure, the strategy is not widely accepted, even among researchers in the aging field. “On the surface it sounds completely insane, but I was surprised how good a case he could make for it,” says Matthew Scholz, CEO of aging research company Oisín Biotechnologies, who met with Hébert this year. 

Scholz is still skeptical though. “A new brain is not going to be a popular item,” he says. “The surgical element of it is going to be very severe, no matter how you slice it.”

Now, though, Hébert's ideas appear to have gotten a huge endorsement from the US government. Hébert told MIT Technology Review that he had proposed a $110 million project to ARPA-H to prove his ideas in monkeys and other animals, and that the government “didn’t blink” at the figure. 

ARPA-H confirmed this week that it had hired Hébert as a program manager. 

The agency, modeled on DARPA, the Department of Defense organization that developed stealth fighters, gives managers unprecedented leeway in awarding contracts to develop novel technologies. Among its first programs are efforts to develop at-home cancer tests and cure blindness with eye transplants .

President Biden created ARPA-H in 2022 to pursue “bold, urgent innovation” with transformative potential.

It may be several months before details of the new project are announced, and it’s possible that ARPA-H will establish more conventional goals like treating stroke victims and Alzheimer’s patients, whose brains are damaged, rather than the more radical idea of extreme life extension. 

“ If it can work, forget aging; it would be useful for all kinds of neurodegenerative disease,” says Justin Rebo, a longevity scientist and entrepreneur.

But defeating death is Hébert's stated aim. “I was a weird kid and when I found out that we all fall apart and die, I was like, ‘Why is everybody okay with this?’ And that has pretty much guided everything I do,” he says. “I just prefer life over this slow degradation into nonexistence that biology has planned for all of us.”

Hébert, now 58, also recalls when he began thinking that the human form might not be set in stone. It was upon seeing the 1973 movie Westworld , in which the gun-slinging villain, played by Yul Brynner, turns out to be an android. “That really stuck with me,” Hébert said.

Lately, Hébert has become something of a star figure among immortalists, a fringe community devoted to never dying. That’s because he’s an established scientist who is willing to propose extreme steps to avoid death. “A lot of people want radical life extension without a radical approach. People want to take a pill, and that’s not going to happen,” says Kai Micah Mills, who runs a company, Cryopets, developing ways to deep-freeze cats and dogs for future reanimation.

The reason pharmaceuticals won’t ever stop aging, Hébert says, is that time affects all of our organs and cells and even degrades substances such as elastin, one of the molecular glues that holds our bodies together. So even if, say, gene therapy could rejuvenate the DNA inside cells, a concept some companies are exploring , Hébert believes we’re still doomed as the scaffolding around them comes undone.

One organization promoting Hébert's ideas is the Longevity Biotech Fellowship (LBF), a self-described group of “hardcore” life extension enthusiasts, which this year published a technical roadmap for defeating aging altogether. In it, they used data from Hébert's ARPA-H proposal to argue in favor of extending life with gradual brain replacement for elderly subjects, as well as transplant of their heads onto the bodies of “non-sentient” human clones, raised to lack a functioning brain of their own, a procedure they referred to as “body transplant.”

Such a startling feat would involve several technologies that don’t yet exist, including a means to attach a transplanted head to a spinal cord. Even so, the group rates “replacement” as the most likely way to conquer death, claiming it would take only 10 years and $3.6 billion to demonstrate.

“It doesn’t require you to understand aging,” says Mark Hamalainen, co-founder of the research and education group. “That is why Jean’s work is interesting.”

Hébert's connections to such far-out concepts (he serves as a mentor in LBF’s training sessions) could make him an edgy choice for ARPA-H, a young agency whose budget is $1.5 billion a year.

For instance, Hebert recently said on a podcast with Hamalainen that human fetuses might be used as a potential source of life-extending parts for elderly people. That would be ethical to do, Hébert said during the program, if the fetus is young enough that there “are no neurons, no sentience, and no person.” And according to a meeting agenda viewed by MIT Technology Review , Hébert was also a featured speaker at an online pitch session held last year on full “body replacement,” which included biohackers and an expert in primate cloning.

Hébert declined to describe the session, which he said was not recorded “out of respect for those who preferred discretion.” But he’s in favor of growing non-sentient human bodies. “I am in conversation with all these groups because, you know, not only is my brain slowly deteriorating, but so is the rest of my body,” says Hébert. “I'm going to need other body parts as well.”

The focus of Hébert's own scientific work is the neocortex, the outer part of the brain that looks like a pile of extra-thick noodles and which houses most of our senses, reasoning, and memory. The neocortex is “arguably the most important part of who we are as individuals,” says Hébert, as well as “maybe the most complex structure in the world.”

There are two reasons he believes the neocortex could be replaced, albeit only slowly. The first is evidence from rare cases of benign brain tumors, like a man described in the medical literature who developed a growth the size of an orange. Yet because it grew very slowly, the man’s brain was able to adjust, shifting memories elsewhere, and his behavior and speech never seemed to change—even when the tumor was removed. 

That’s proof, Hébert thinks, that replacing the neocortex little by little could be achieved “without losing the information encoded in it” such as a person’s self-identity.

The second source of hope, he says, is experiments showing that fetal-stage cells can survive, and even function, when transplanted into the brains of adults. For instance, medical tests underway are showing that young neurons can integrate into the brains of people who have epilepsy  and stop their seizures.  

“It was these two things together—the plastic nature of brains and the ability to add new tissue—that, to me, were like, ‘Ah, now there has got to be a way,’” says Hébert.

“I just prefer life over this slow degradation into nonexistence that biology has planned for all of us.”

One challenge ahead is how to manufacture the replacement brain bits, or what Hebert has called “facsimiles” of neocortical tissue. During a visit to his lab at Albert Einstein, Hébert described plans to manually assemble chunks of youthful brain tissue using stem cells. These parts, he says, would not be fully developed, but instead be similar to what’s found in a still-developing fetal brain. That way, upon transplant, they’d be able to finish maturing, integrate into your brain, and be “ready to absorb and learn your information.”

To design the youthful bits of neocortex, Hébert has been studying brains of aborted human fetuses 5 to 8 weeks of age. He’s been measuring what cells are present, and in what numbers and locations, to try to guide the manufacture of similar structures in the lab.

“What we're engineering is a fetal-like neocortical tissue that has all the cell types and structure needed to develop into normal tissue on its own,” says Hébert. 

Part of the work has been carried out by a startup company, BE Therapeutics (it stands for Brain Engineering), located in a suite on Einstein’s campus and which is funded by Apollo Health Ventures, VitaDAO, and with contributions from a New York State development fund . The company had only two employees when MIT Technology Review visited this spring, and the its future is uncertain, says Hébert, now that he’s joining ARPA-H and closing his lab at Einstein.

Because it’s often challenging to manufacture even a single cell type from stem cells, making a facsimile of the neocortex involving a dozen cell types isn’t an easy project . In fact, it’s just one of several scientific problems standing between you and a younger brain, some of which might never have practical solutions. “There is a saying in engineering. You are allowed one miracle, but if you need more than one, find another plan,” says Scholz.

Maybe the crucial unknown is whether young bits of neocortex will ever correctly function inside an elderly person’s brain, for example by establishing connections or storing and sending electro-chemical information. Despite evidence the brain can incorporate individual transplanted cells, that’s never been robustly proven for larger bits of tissue, says Rusty Gage, a biologist at the Salk Institute in La Jolla, Calif., and who is considered a pioneer of neural transplants. He says researchers for years have tried to transplant larger parts of fetal animal brains into adult animals, but with inconclusive results. “If it worked, we’d all be doing more of it,” he says.

The problem, says Gage, isn’t whether the tissue can survive, but whether it can participate in the workings of an existing brain. “I am not dissing his hypothesis. But that’s all it is,” says Gage. “Yes, fetal or embryonic tissue can mature in the adult brain. But whether it replaces the function of the dysfunctional area is an experiment he needs to do, if he wants to convince the world he has actually replaced an aged section with a new section.”

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