what is population and sampling in research

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• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Guide to population vs. sample in research

Last updated

29 May 2023

Reviewed by

Miroslav Damyanov

Population data consists of information collected from every individual in a particular population. Meanwhile, sample data consists of information taken from a subset—or sample —of the population.

In this guide, we’ll discuss the differences between population and sample data, the advantages and disadvantages of each, how to collect data from a sample and a population, and common sampling techniques . By the end, you'll have a better understanding of the differences between population and sample data and when to use them.

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• What is "population" in research?

Population data is the total number of measurements taken from every individual within a group. For example, if you were measuring the heights of all humans on Earth, you’d include all 7 billion people in your population data set. 

When analyzing population data, researchers use statistics such as the population mean, median, and standard deviation. 

Types of populations

Finite population.

A finite population is a population in which all the members are known and can be counted. Examples of this type of population include all the employees of a company, all the students in a school, or the entire population of a city. When working with a finite population, you can calculate the exact population mean, median, and standard deviation.

Infinite population

An infinite population is a population that is too large to be measured or counted. This could be the entire human population on Earth or the number of stars in the sky. Because it’s impossible to measure or count these populations, it isn’t possible to calculate their exact mean, median, and standard deviation.

Closed population

A closed population is one in which you allow no new members to join. An example of a closed population would be a country's citizens over the age of 18 who have been living there for more than 10 years. As no new members can join, the population remains constant and can easily be measured and analyzed.

Open population

An open population is one in which new members can join. For example, all people living in a certain city are considered an open population because new members can move into the city and become part of the population. This type of population is constantly changing, so it isn’t possible to measure and analyze its exact characteristics.

Advantages of population data

Representative.

It offers a complete representation of all elements in the population, which can increase the generalizability of findings.

High quality

Population data is usually very accurate and detailed because standardized data collection methods and quality control measures are in place to provide data from every element in the population.

Large sample size

The sample size is large, which can increase the statistical power of a study and help detect small but meaningful differences. 

Can address rare events

You can use population data to study rare events or diseases that wouldn’t be feasible to study through other methods.

Allows for subgroup analysis

You can use population data to examine subgroups of the population, which can help identify disparities and inform interventions. 

Disadvantages of population data

Time and cost constraints.

Collecting data from a large population is expensive and time-consuming, especially when it comes to data cleaning and preparation before using it for analysis.

Limited access

Depending on the source of population data, it can be difficult to get access to the population or convince people to participate, especially when there are privacy concerns or restrictions on the use of data.

Limited variables

Population data may have limited variables or lack information on important factors, which may not allow one to answer a particular research question if the data wasn’t originally collected for that purpose.

Difficult to analyze

Population data can be large, complex, and contain a wide variety of data or even missing data which demands advanced analytical skills and high computational requirements. 

Outdated information

Population data may become outdated, especially if it was collected some time ago, which can limit its relevance to current research questions. 

• What is a sample in research?

Sampling is the process of selecting individuals from a larger population and is used to generate representative information about the population of interest. There are two forms of sampling: non-probability. 

Probability sampling is from a randomly selected small subset and provides statistical inferences about the whole population without bias. Non-probability sampling collects data from a selected subset chosen for its convenience or, sometimes, to control and manipulate the data collected.

Types of probability sampling

Random sampling.

This type of sampling is completely by chance. Each member of the population has an equal chance of being selected for the sample, and the results of a random sample will be statistically representative of the whole population. 

For example, if you wanted to know how people felt about a new product, you could use a random number generator to select members from a population for the study.

Stratified sampling

Stratified sampling is when the population is split into different subgroups, or strata, based on one or more characteristics. The researcher then randomly selects members from each stratum to represent the population. This allows the researcher to accurately compare data between different groups because it ensures that all subgroups are represented in the sample. 

For example, if you wanted to measure the opinion of people in different age groups, you could divide your population into groups based on age and then take random samples from each stratum.

Cluster sampling

This type of sampling divides the population into clusters or groups and then further takes a sample from each cluster. This method is often used when it isn’t possible to access the entire population. 

For example, if you wanted to measure public opinion on an issue in a large city, it wouldn’t be feasible to survey every single person. Instead, you could divide the city into neighborhoods and take random samples from each one.

Systematic sample

Systematic sampling involves selecting items from a population based on a set pattern or system. This type of sampling is useful when it’s impossible or impractical to create a list of all items in a population. It’s similar to random sampling in that it helps eliminate any bias from the selection process, but it’s more efficient because it requires fewer samples to be taken. 

If a researcher can only select 10 members from a population of 200 people, they could use systematic sampling by selecting every 20th person in the list to eliminate bias.

Types of non-probability sampling

Convenience sampling.

This form of sampling involves selecting participants based on availability and willingness to take part. This can lead to volunteer bias, meaning that individuals who are more motivated or have more time may be more likely to participate.

Quota sampling

A method of selecting participants from a larger population to match certain criteria is referred to as quota sampling. For example, market researchers might use quota sampling to select a certain number of individuals within specific age groups.

Judgemental sampling

This technique is also referred to as purposive sampling or authoritative sampling. You can use it to target specific individuals who possess a certain set of qualities like age, ethnicity, or religious beliefs. It can help researchers access important information from people with specific knowledge or experience. 

However, this kind of sampling can also lead to selection bias, which is the distortion of results due to the non-random selection of participants.

Snowball sampling

Snowball sampling is often used to reach individuals who may be difficult to access through traditional means. This type of sampling involves asking participants to refer others who fit the same criteria. It’s often used in social sciences research to identify people within a certain community or social group. For example, researchers may conduct a survey offering a reward to participants who refer their close friends or family and get them to participate.  

While this technique can be useful in reaching underserved or underrepresented populations, it also carries the risk of selection bias.

Advantages of sample data

Cost-effective.

Collecting data from a sample is typically less expensive and time-consuming than collecting data from an entire population.  

Higher quality

Collecting data from a smaller subset of a population can often result in higher-quality data when more resources are dedicated to ensuring the accuracy and completeness of the data. 

Feasibility

In some cases, it may be impossible or impractical to collect data from an entire population, making sample data a more feasible option. 

Sample data is usually smaller and more manageable than population data, which makes it easier to analyze. 

Reduced sampling bias

With appropriate sampling methods, sample data can be representative of the large population and provide valuable insights for research. 

Disadvantages of using sample data

Generalizability.

The quality of the data depends on the quality of the sample selection process. If the sample isn’t representative of the population, it leads to skewed results.

Sampling bias

A sample may not provide a complete picture of an entire population when certain groups are overrepresented or underrepresented in the sample.  

Sampling error

Because sample data is drawn from a subset of a larger population, there is always a risk of sampling error . It occurs when the sample doesn’t accurately represent the larger population, which can lead to inaccurate results.

Statistical power

A small sample size can limit the statistical power of the data analysis, making it more difficult to detect meaningful differences or relationships between studied variables. 

Limited score

Sample data may be limited in scope and may not capture the full range of variables present in an entire population. This can limit the depth and breadth of the findings.

• Differences between population and sample

When discussing research and data analysis, it’s important to understand the differences between population and sample data. Here are some key points to consider when distinguishing between the two: 

Population vs. sample

A population is a set of all individuals or objects that share a common characteristic, while a sample is a subset of that population used to draw conclusions about the entire population. 

For example, if you wanted to research the opinions of all people living in the United States, the population would be all citizens in the US, while the sample would be a smaller subset of people surveyed to represent the opinion of the entire population.

Sample vs. population mean

The sample mean is an average of a sample's values, while the population mean is an average of all values in a population. For example, if you’re researching the average income of households in America, the sample mean would be an average of incomes from a smaller group of households selected from the population of all households in the US.

Sample vs. population standard deviation

Standard deviation measures the variation of a set of values from their mean. The sample standard deviation is based on the variation within a sample, while the population standard deviation is based on the variation within a population. 

For example, if you were researching the variation in test scores for students at a particular school, the sample standard deviation would be based on the scores of a smaller subset of students from the school, while the population standard deviation would be based on all scores from every student at the school.

• How to collect and use data from a sample

1. Choose the right sampling technique

The most common sampling techniques include random, stratified, convenience, and cluster sampling . Selecting the right technique for your research will depend on your specific needs, resources, goals, and objectives.

2. Decide the sample size

Determining the sample size will vary depending on the goal of your research. Generally speaking, the larger the sample size, the more reliable your results will be. However, there are tradeoffs, such as the cost and resources required to collect data from larger samples.

3. Design an instrument for collecting data

Once you've chosen your sampling technique and decided on the sample size, you'll need to design an instrument for collecting data. This could include surveys , interviews, or experiments. Make sure that the instrument is valid and reliable so that it provides accurate results.

4. Determine a sample frame 

Decide who you’ll include in the sample by selecting the population or subpopulation you want to study. Consider factors like location, age, gender, behavior, and so on when choosing your sample frame.

5. Execute the sample selection process

In this step, you'll select individuals to form your sample. To ensure accuracy, it’s best to use random sampling techniques to guarantee a representative sample.

6. Collect data from a sample

Once you’ve selected the sample, you can begin collecting data. Depending on the method you chose (e.g., survey, interview, experiment), you may need to do some additional steps before you can begin collecting data:

For example, if you’re collecting data through a survey, you may need to obtain permission to conduct the survey from relevant authorities, such as a workplace or community group.

If you plan to conduct interviews as your data collection method, ensure your questions are well-formed and that your interviewees are comfortable answering them. Before the interview, you may also want to send a pre-interview questionnaire to participants to collect basic information to make the interview process more efficient.

Most experiments require a significant amount of planning and preparation to ensure that data is collected in a controlled and systematic manner. Additionally, you may need to consider the ethical implications of conducting the experiment, such as obtaining informed consent from participants and ensuring their safety throughout the experiment.

7. Analyze the data

After you've collected data from the sample, analyze it to find meaningful patterns and trends that you can use to draw conclusions about the population. Remember, since you're working with a sample, your conclusions may not apply to the entire population. 

By following these steps, you can easily collect data from a sample to gain insights about a population without having to analyze all of the data from the population itself. When used correctly, sample data can provide valuable insights that can help shape your research conclusions.

• How to collect and use data from a population

1. Define the population

Before collecting data from a population, it’s important to first clearly define what population you’re looking to collect data from. This definition should be as specific as possible and include any relevant behavioral characteristics (e.g., shopping frequency, product use, or commute options) or demographic characteristics (e.g., age, gender, and geography).

2. Create a comprehensive list

After identifying the population in terms of traits, past experiences, outlooks, or other components, create a comprehensive list of the population you’ll be studying. Depending on the purpose of the study, this could include both people and organizations.

3. Contact population and collect data

Once you’ve defined the population and chosen your sampling method, it’s time to collect data. You can obtain this data by conducting experiments, surveys, or interviews. Make sure to collect feedback from every person or entity on the population list to generate an exhaustive population sample.

4. Analyze the data

After collecting the data, it’s important to analyze it to draw meaningful conclusions about the population. This analysis should include calculating the sample mean and sample standard deviation for the data set, as well as comparing these values to the population mean and population standard deviation.

5. Draw conclusions

Once you’ve analyzed the data, use the results to draw conclusions about the population. Make sure to be as accurate and objective as possible when making claims about the population.

• Choosing high-quality samples

High-quality samples are essential when it comes to research. A high-quality sample will produce accurate and reliable study results. A poor-quality sample can result in incorrect or inexact data. These results can be costly and time-consuming to fix. 

A good-quality sample is representative of the population. That means the sample has similar characteristics as the population in terms of age, gender, race, and other factors. The sample should also be randomly selected so as not to bias the results. In addition, the sample should be of a large enough size to be statistically significant .

How to select a high-quality sample

Choose a probability sampling method.

Random selection is the most important part of choosing a high-quality sample. You want to ensure that the sample truly represents the population and that no bias has been introduced. You can do this through methods such as random sampling, stratified sampling, cluster sampling, and systematic sampling. 

Monitor selection process

You should monitor the selection process to ensure that no bias has been introduced during the selection process. You should also make sure that the sample size is large enough to be statistically significant. 

Test for accuracy

You should test the accuracy of your sample by comparing it to the population data. Compare the sample mean vs. population mean, sample vs. population standard deviation, and other factors. If there are any discrepancies between the two, then the sample may not be representative of the population and should be re-evaluated.

By following these steps, you can ensure that your sample is quality and that it correctly reflects the population and produces precise and accurate results.

Using sample and population data can be beneficial in many ways. For example, using sample data allows researchers to make more efficient use of resources while still being able to conclude the population. Additionally, sample data is useful in making statistical inferences about a population, such as the mean or standard deviation. 

On the other hand, population data provides an accurate representation of the whole population, which can be beneficial when researchers need detailed information. 

To ensure accurate and representative data, researchers must understand the differences between populations and weigh the advantages and risks of each sampling technique. By understanding the difference between population and sample data, researchers can gain valuable insights about their target group and use these insights to make informed decisions.

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Population and samples: the complete guide.

9 min read What are the differences between populations and samples? In this guide, we’ll discuss the two, as well as how to use them effectively in your research.

When we hear the term population, the first thing that comes to mind is a large group of people.

In market research, however, a population is an entire group that you want to draw conclusions about and possesses a standard parameter that is consistent throughout the group.

It’s important to note that a population doesn’t always refer to people, it can mean anything you want to study: objects, organizations, animals, chemicals and so on.

For example, all the countries in the world are an example of a population — or even the number of males in the UK. The size of the population can vary according to the target entities in question and the scope of the research.

When do you need to collect data from a population?

You use populations when your research calls for or requires you to collect data from every member of the population. Note: it’s normally far easier to collect data from whole populations when they’re small and accessible.

For larger and more diverse populations, on the other hand — e.g. a regional study on people living in Europe — while you would get findings representative of the entire population (as they’re all included in the study), it would take a considerable amount of time.

It’s in these instances that you use sampling. It allows you to make more precise inferences about the population as a whole, and streamline your research project. They’re typically used when population sizes are too large to include all possible members or inferences.

Let’s talk about samples.

What is a sample?

In statistical methods, a sample consists of a smaller group of entities, which are taken from the entire population. This creates a subset group that is easier to manage and has the characteristics of the larger population.

This smaller subset is then surveyed to gain information and data. The sample should reflect the population as a whole, without any bias towards a specific attribute or characteristic. In this way, researchers can ensure their results are representative and statistically significant.

To remove unconscious selection bias, a researcher may choose to randomize the selection of the sample.

Types of samples

There are two categories of sampling generally used – probability sampling and non-probability sampling :

• Probability sampling , also known as random sampling, is a kind of sample selection where randomization is used instead of deliberate choice.
• Non-probability sampling techniques involve the researcher deliberately picking items or individuals for the sample based on their research goals or knowledge

These two sampling techniques have several methods:

Probability sampling types include:

• Simple random sampling Every element in the population has an equal chance of being selected as part of the sample. Find out more about simple random sampling.
• Systematic sampling Also known as systematic clustering, in this method, random selection only applies to the first item chosen. A rule then applies so that every nth item or person after that is picked. Find out more about systematic sampling .
• Stratified random sampling Sampling uses random selection within predefined groups. Find out more about stratified random sampling .
• Cluster sampling Groups rather than individual units of the target population are selected at random.

Non-probability sampling types include:

• Convenience sampling People or elements in a sample are selected based on their availability.
• Quota sampling The sample is formed according to certain groups or criteria.
• Purposive sampling Also known as judgmental sampling. The sample is formed by the researcher consciously choosing entities, based on the survey goals.
• Snowball sampling Also known as referral sampling. The sample is formed by sample participants recruiting connections.

Find out more about sampling methods with our ultimate guide to sampling methods and best practices

Calculating sample size

Worried about sample sizes? You can also use our sample size calculator to determine how many responses you need to be confident in your data.

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When to use sampling

As mentioned, sampling is useful for dealing with population data that is too large to process as a whole or is inaccessible. Sampling also helps to keep costs down and reduce time to insight.

Advantages of using sampling to collect data

• Provide researchers with a representative view of the population through the sample subset.
• The researcher has flexibility and control over what kind of sample they want to make, depending on their needs and the goals of the research.
• Reduces the volume of data, helping to save time.
• With proper methods, researchers can achieve a higher level of accuracy
• Researchers can get detailed information on a population with a smaller amount of resource
• Significantly cheaper than other methods
• Allows for deeper study of some aspects of data — rather than asking 15 questions to every individual, it’s better to use 50 questions on a representative sample

Disadvantages of using sampling to collect data

• Researcher bias can affect the quality and accuracy of results
• Sampling studies require well-trained experts
• Even with good survey design, there’s no way to eliminate sampling errors entirely
• People in the sample may refuse to respond
• Probability sampling methods can be less representative in favor of random allocation.
• Improper selection of sampling techniques can affect the entire process negatively

How can you use sampling in business?

Depending on the nature of your study and the conclusions you wish to draw, you’ll have to select an appropriate sampling method as mentioned above. That said, here are a few examples of how you can use sampling techniques in business.

Creating a new product

If you’re looking to create a new product line, you may want to do panel interviews or surveys with a representative sample for the new market. By showing your product or concept to a sample that represents your target audience (population), you ensure that the feedback you receive is more reflective of how that customer segment will feel.

Average employee performance

If you wanted to understand the average employee performance for a specific group, you could use a random sample from a team or department (population). As every person in the department has a chance of being selected, you’ll have a truly random — yet representative sample. From the data collected, you can make inferences about the team/department’s average performance.

Store feedback

Let’s say you want to collect feedback from customers who are shopping or have just finished shopping at your store. To do this, you could use convenience sampling. It’s fast, affordable and done at a point of convenience. You can use this to get a quick gauge of how people feel about your store’s shopping experience — but it won’t represent the true views of all your customers.

Manage your population and sample data easily

Whatever the sample size of your target audience, there are several things to consider:

• How can you save time in conducting the research?
• How do you analyze and compare all the responses?
• How can you track and chase non-respondents easily?
• How can you translate the data into a usable presentation format?
• How can you share this easily?

These questions can make the task of supporting internal teams and management difficult.

This is where the Qualtrics CoreXM technology solution can help you progress through research with ease.

It includes:

• Advanced AI and machine learning tools to easily analyze data from open-text responses and data, giving you actionable insights at scale.
• Intuitive drag-and-drop survey building with powerful logic, 100+ question types, and pre-built survey templates . For more information on how to get started on your survey creation, visit our complete guide on creating a survey.
• Stylish, accessible and easy-to-understand reporting that automatically updates in real time, so everyone in your organization has the latest insights at their fingertips.
• Powerful automation to get up and running quickly with out-of-the-box workflows, including guided setup and proactive recommendations to help you connect with other teams and react fast to changes.

Also, the Qualtrics online research panels and samples help you to:

• Choose a target audience and get access to a representative sample
• Boost the accuracy of your research with a sample methodology that’s 47% more consistent than standard sampling methods
• Get dedicated support at every stage, from launching your survey to reporting on the results.

Want to learn more?

Related resources

Panels & Samples

Representative Samples 13 min read

Reward survey participants 15 min read, panel management 14 min read, what is a research panel 10 min read.

Analysis & Reporting

Data Saturation In Qualitative Research 8 min read

How to determine sample size 12 min read.

Market Segmentation

User Personas 14 min read

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3. Populations and samples

Populations, unbiasedness and precision, randomisation, variation between samples, standard error of the mean.

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Statistics without tears: Populations and samples

Amitav banerjee.

Department of Community Medicine, D Y Patil Medical College, Pune, India

Suprakash Chaudhury

1 Department of Psychiatry, RINPAS, Kanke, Ranchi, India

Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized. In actual practice, the task is so difficult that some sampling bias occurs in almost all studies to a lesser or greater degree. In order to assess the degree of this bias, the informed reader of medical literature should have some understanding of the population from which the sample was drawn. The ultimate decision on whether the results of a particular study can be generalized to a larger population depends on this understanding. The subsequent deliberations dwell on sampling strategies for different types of research and also a brief description of different sampling methods.

Research workers in the early 19th century endeavored to survey entire populations. This feat was tedious, and the research work suffered accordingly. Current researchers work only with a small portion of the whole population (a sample) from which they draw inferences about the population from which the sample was drawn.

This inferential leap or generalization from samples to population, a feature of inductive or empirical research, can be full of pitfalls. In clinical medicine, it is not sufficient merely to describe a patient without assessing the underlying condition by a detailed history and clinical examination. The signs and symptoms are then interpreted against the total background of the patient's history and clinical examination including mental state examination. Similarly, in inferential statistics, it is not enough to just describe the results in the sample. One has to critically appraise the real worth or representativeness of that particular sample. The following discussion endeavors to explain the inputs required for making a correct inference from a sample to the target population.

TARGET POPULATION

Any inferences from a sample refer only to the defined population from which the sample has been properly selected. We may call this the target population. For example, if in a sample of lawyers from Delhi High Court it is found that 5% are having alcohol dependence syndrome, can we say that 5% of all lawyers all over the world are alcoholics? Obviously not, as the lawyers of Delhi High Court may be an institution by themselves and may not represent the global lawyers′ community. The findings of this study, therefore, apply only to Delhi High Court lawyers from which a representative sample was taken. Of course, this finding may nevertheless be interesting, but only as a pointer to further research. The data on lawyers in a particular city tell us nothing about lawyers in other cities or countries.

POPULATIONS IN INFERENTIAL STATISTICS

In statistics, a population is an entire group about which some information is required to be ascertained. A statistical population need not consist only of people. We can have population of heights, weights, BMIs, hemoglobin levels, events, outcomes, so long as the population is well defined with explicit inclusion and exclusion criteria. In selecting a population for study, the research question or purpose of the study will suggest a suitable definition of the population to be studied, in terms of location and restriction to a particular age group, sex or occupation. The population must be fully defined so that those to be included and excluded are clearly spelt out (inclusion and exclusion criteria). For example, if we say that our study populations are all lawyers in Delhi, we should state whether those lawyers are included who have retired, are working part-time, or non-practicing, or those who have left the city but still registered at Delhi.

Use of the word population in epidemiological research does not correspond always with its demographic meaning of an entire group of people living within certain geographic or political boundaries. A population for a research study may comprise groups of people defined in many different ways, for example, coal mine workers in Dhanbad, children exposed to German measles during intrauterine life, or pilgrims traveling to Kumbh Mela at Allahabad.

GENERALIZATION (INFERENCES) FROM A POPULATION

When generalizing from observations made on a sample to a larger population, certain issues will dictate judgment. For example, generalizing from observations made on the mental health status of a sample of lawyers in Delhi to the mental health status of all lawyers in Delhi is a formalized procedure, in so far as the errors (sampling or random) which this may hazard can, to some extent, be calculated in advance. However, if we attempt to generalize further, for instance, about the mental statuses of all lawyers in the country as a whole, we hazard further pitfalls which cannot be specified in advance. We do not know to what extent the study sample and population of Delhi is typical of the larger population – that of the whole country – to which it belongs.

The dilemmas in defining populations differ for descriptive and analytic studies.

POPULATION IN DESCRIPTIVE STUDIES

In descriptive studies, it is customary to define a study population and then make observations on a sample taken from it. Study populations may be defined by geographic location, age, sex, with additional definitions of attributes and variables such as occupation, religion and ethnic group.[ 1 ]

Geographic location

In field studies, it may be desirable to use a population defined by an administrative boundary such as a district or a state. This may facilitate the co-operation of the local administrative authorities and the study participants. Moreover, basic demographic data on the population such as population size, age, gender distribution (needed for calculating age- and sex-specific rates) available from census data or voters’ list are easier to obtain from administrative headquarters. However, administrative boundaries do not always consist of homogenous group of people. Since it is desirable that a modest descriptive study does not cover a number of different groups of people, with widely differing ways of life or customs, it may be necessary to restrict the study to a particular ethnic group, and thus ensure better genetic or cultural homogeneity. Alternatively, a population may be defined in relation to a prominent geographic feature, such as a river, or mountain, which imposes a certain uniformity of ways of life, attitudes, and behavior upon the people who live in the vicinity.

If cases of a disease are being ascertained through their attendance at a hospital outpatient department (OPD), rather than by field surveys in the community, it will be necessary to define the population according to the so-called catchment area of the hospital OPD. For administrative purposes, a dispensary, health center or hospital is usually considered to serve a population within a defined geographic area. But these catchment areas may only represent in a crude manner with the actual use of medical facilities by the local people. For example, in OPD study of psychiatric illnesses in a particular hospital with a defined catchment area, many people with psychiatric illnesses may not visit the particular OPD and may seek treatment from traditional healers or religious leaders.

Catchment areas depend on the demography of the area and the accessibility of the health center or hospital. Accessibility has three dimensions – physical, economic and social.[ 2 ] Physical accessibility is the time required to travel to the health center or medical facility. It depends on the topography of the area (e.g. hill and tribal areas with poor roads have problems of physical accessibility). Economic accessibility is the paying capacity of the people for services. Poverty may limit health seeking behavior if the person cannot afford the bus fare to the health center even if the health services may be free of charge. It may also involve absence from work which, for daily wage earners, is a major economic disincentive. Social factors such as caste, culture, language, etc. may adversely affect accessibility to health facility if the treating physician is not conversant with the local language and customs. In such situations, the patient may feel more comfortable with traditional healers.

Ascertainment of a particular disease within a particular area may be incomplete either because some patient may seek treatment elsewhere or some patients do not seek treatment at all. Focus group discussions (qualitative study) with local people, especially those residing away from the health center, may give an indication whether serious underreporting is occurring.

When it is impossible to relate cases of a disease to a population, perhaps because the cases were ascertained through a hospital with an undefined catchment area, proportional morbidity rates may be used. These rates have been widely used in cancer epidemiology where the number of cases of one form of cancer is expressed as a proportion of the number of cases of all forms of cancer among patients attending the same hospital during the same period.

POPULATIONS IN ANALYTIC STUDIES

Case control studies.

As opposed to descriptive studies where a study population is defined and then observations are made on a representative sample from it, in case control studies observations are made on a group of patients. This is known as the study group , which usually is not selected by sampling of a defined larger group. For instance, a study on patients of bipolar disorder may include every patient with this disorder attending the psychiatry OPD during the study period. One should not forget, however, that in this situation also, there is a hypothetical population consisting of all patients with bipolar disorder in the universe (which may be a certain region, a country or globally depending on the extent of the generalization intended from the findings of the study). Case control studies are often carried out in hospital settings because this is more convenient and accessible group than cases in the community at large. However, the two groups of cases may differ in many respects. At the outset of the study, it should be deliberated whether these differences would affect the external validity (generalization) of the study. Usually, analytic studies are not carried out in groups containing atypical cases of the disorder, unless there is a special indication to do so.

Populations in cohort studies

Basically, cohort studies compare two groups of people (cohorts) and demonstrate whether or not there are more cases of the disease among the cohort exposed to the suspected cause than among the cohort not exposed. To determine whether an association exists between positive family history of schizophrenia and subsequent schizophrenia in persons having such a history, two cohorts would be required: first, the exposed group, that is, people with a family history of mental disorders (the suspected cause) and second, the unexposed group, that is, people without a family history of mental disorders. These two cohorts would need to be followed up for a number of years and cases of schizophrenia in either group would be recorded. If a positive family history is associated with development of schizophrenia, then more cases would occur in the first group than in the second group.

The crucial challenges in a cohort study are that it should include participants exposed to a particular cause being investigated and that it should consist of persons who can be followed up for the period of time between exposure (cause) and development of the disorder. It is vital that the follow-up of a cohort should be complete as far as possible. If more than a small proportion of persons in the cohort cannot be traced (loss to follow-up or attrition), the findings will be biased , in case these persons differ significantly from those remaining in the study.

Depending on the type of exposure being studied, there may or may not be a range of choice of cohort populations exposed to it who may form a larger population from which one has to select a study sample. For instance, if one is exploring association between occupational hazard such as job stress in health care workers in intensive care units (ICUs) and subsequent development of drug addiction, one has to, by the very nature of the research question, select health care workers working in ICUs. On the other hand, cause effect study for association between head injury and epilepsy offers a much wider range of possible cohorts.

Difficulties in making repeated observations on cohorts depend on the length of time of the study. In correlating maternal factors (pregnancy cohort) with birth weight, the period of observation is limited to 9 months. However, if in a study it is tried to find the association between maternal nutrition during pregnancy and subsequent school performance of the child, the study will extend to years. For such long duration investigations, it is wise to select study cohorts that are firstly, not likely to migrate, cooperative and likely to be so throughout the duration of the study, and most importantly, easily accessible to the investigator so that the expense and efforts are kept within reasonable limits. Occupational groups such as the armed forces, railways, police, and industrial workers are ideal for cohort studies. Future developments facilitating record linkage such as the Unique Identification Number Scheme may give a boost to cohort studies in the wider community.

A sample is any part of the fully defined population. A syringe full of blood drawn from the vein of a patient is a sample of all the blood in the patient's circulation at the moment. Similarly, 100 patients of schizophrenia in a clinical study is a sample of the population of schizophrenics, provided the sample is properly chosen and the inclusion and exclusion criteria are well defined.

To make accurate inferences, the sample has to be representative. A representative sample is one in which each and every member of the population has an equal and mutually exclusive chance of being selected.

Sample size

Inputs required for sample size calculation have been dealt from a clinical researcher's perspective avoiding the use of intimidating formulae and statistical jargon in an earlier issue of the journal.[ 1 ]

Target population, study population and study sample

A population is a complete set of people with a specialized set of characteristics, and a sample is a subset of the population. The usual criteria we use in defining population are geographic, for example, “the population of Uttar Pradesh”. In medical research, the criteria for population may be clinical, demographic and time related.

• Clinical and demographic characteristics define the target population, the large set of people in the world to which the results of the study will be generalized (e.g. all schizophrenics).
• The study population is the subset of the target population available for study (e.g. schizophrenics in the researcher's town).
• The study sample is the sample chosen from the study population.

METHODS OF SAMPLING

Purposive (non-random samples).

• Volunteers who agree to participate
• Snowball sample, where one case identifies others of his kind (e.g. intravenous drug users)
• Convenient sample such as captive medical students or other readily available groups
• Quota sampling, at will selection of a fixed number from each group
• Referred cases who may be under pressure to participate
• Haphazard with combination of the above methods

Non-random samples have certain limitations. The larger group (target population) is difficult to identify. This may not be a limitation when generalization of results is not intended. The results would be valid for the sample itself (internal validity). They can, nevertheless, provide important clues for further studies based on random samples. Another limitation of non-random samples is that statistical inferences such as confidence intervals and tests of significance cannot be estimated from non-random samples. However, in some situations, the investigator has to make crucial judgments. One should remember that random samples are the means but representativeness is the goal. When non-random samples are representative (compare the socio-demographic characteristics of the sample subjects with the target population), generalization may be possible.

Random sampling methods

Simple random sampling.

A sample may be defined as random if every individual in the population being sampled has an equal likelihood of being included. Random sampling is the basis of all good sampling techniques and disallows any method of selection based on volunteering or the choice of groups of people known to be cooperative.[ 3 ]

In order to select a simple random sample from a population, it is first necessary to identify all individuals from whom the selection will be made. This is the sampling frame. In developing countries, listings of all persons living in an area are not usually available. Census may not catch nomadic population groups. Voters’ and taxpayers’ lists may be incomplete. Whether or not such deficiencies are major barriers in random sampling depends on the particular research question being investigated. To undertake a separate exercise of listing the population for the study may be time consuming and tedious. Two-stage sampling may make the task feasible.

The usual method of selecting a simple random sample from a listing of individuals is to assign a number to each individual and then select certain numbers by reference to random number tables which are published in standard statistical textbooks. Random number can also be generated by statistical software such as EPI INFO developed by WHO and CDC Atlanta.

Systematic sampling

A simple method of random sampling is to select a systematic sample in which every n th person is selected from a list or from other ordering. A systematic sample can be drawn from a queue of people or from patients ordered according to the time of their attendance at a clinic. Thus, a sample can be drawn without an initial listing of all the subjects. Because of this feasibility, a systematic sample may have some advantage over a simple random sample.

To fulfill the statistical criteria for a random sample, a systematic sample should be drawn from subjects who are randomly ordered. The starting point for selection should be randomly chosen. If every fifth person from a register is being chosen, then a random procedure must be used to determine whether the first, second, third, fourth, or fifth person should be chosen as the first member of the sample.

Multistage sampling

Sometimes, a strictly random sample may be difficult to obtain and it may be more feasible to draw the required number of subjects in a series of stages. For example, suppose we wish to estimate the number of CATSCAN examinations made of all patients entering a hospital in a given month in the state of Maharashtra. It would be quite tedious to devise a scheme which would allow the total population of patients to be directly sampled. However, it would be easier to list the districts of the state of Maharashtra and randomly draw a sample of these districts. Within this sample of districts, all the hospitals would then be listed by name, and a random sample of these can be drawn. Within each of these hospitals, a sample of the patients entering in the given month could be chosen randomly for observation and recording. Thus, by stages, we draw the required sample. If indicated, we can introduce some element of stratification at some stage (urban/rural, gender, age).

It should be cautioned that multistage sampling should only be resorted to when difficulties in simple random sampling are insurmountable. Those who take a simple random sample of 12 hospitals, and within each of these hospitals select a random sample of 10 patients, may believe they have selected 120 patients randomly from all the 12 hospitals. In statistical sense, they have in fact selected a sample of 12 rather than 120.[ 4 ]

Stratified sampling

If a condition is unevenly distributed in a population with respect to age, gender, or some other variable, it may be prudent to choose a stratified random sampling method. For example, to obtain a stratified random sample according to age, the study population can be divided into age groups such as 0–5, 6–10, 11–14, 15–20, 21–25, and so on, depending on the requirement. A different proportion of each group can then be selected as a subsample either by simple random sampling or systematic sampling. If the condition decreases with advancing age, then to include adequate number in the older age groups, one may select more numbers in older subsamples.

Cluster sampling

In many surveys, studies may be carried out on large populations which may be geographically quite dispersed. To obtain the required number of subjects for the study by a simple random sample method will require large costs and will be cumbersome. In such cases, clusters may be identified (e.g. households) and random samples of clusters will be included in the study; then, every member of the cluster will also be part of the study. This introduces two types of variations in the data – between clusters and within clusters – and this will have to be taken into account when analyzing data.

Cluster sampling may produce misleading results when the disease under study itself is distributed in a clustered fashion in an area. For example, suppose we are studying malaria in a population. Malaria incidence may be clustered in villages having stagnant water collections which may serve as a source of mosquito breeding. In villages without such water stagnation, there will be lesser malaria cases. The choice of few villages in cluster sampling may give erroneous results. The selection of villages as a cluster may be quite unrepresentative of the whole population by chance.[ 5 ]

Lot quality assurance sampling

Lot quality assurance sampling (LQAS), which originated in the manufacturing industry for quality control purposes, was used in the nineties to assess immunization coverage, estimate disease prevalence, and evaluate control measures and service coverage in different health programs.[ 6 ] Using only a small sample size, LQAS can effectively differentiate between areas that have or have not met the performance targets. Thus, this method is used not only to estimate the coverage of quality care but also to identify the exact subdivisions where it is deficient so that appropriate remedial measures can be implemented.

The choice of sampling methods is usually dictated by feasibility in terms of time and resources. Field research is quite messy and difficult like actual battle. It may be sometimes difficult to get a sample which is truly random. Most samples therefore tend to get biased. To estimate the magnitude of this bias, the researcher should have some idea about the population from which the sample is drawn. In conclusion, the following quote cited by Bradford Hill[ 4 ] elegantly sums up the benefit of random sampling:

…The actual practice of medicine is virtually confined to those members of the population who either are ill, or think they are ill, or are thought by somebody to be ill, and these so amply fill up the working day that in the course of time one comes unconsciously to believe that they are typical of the whole. This is not the case. The use of a random sample brings to light the individuals who are ill and know they are ill but have no intention of doing anything about it, as well as those who have never been ill, and probably never will be until their final illness. These would have been inaccessible to any other method of approach but that of the random sample… . J. H. Sheldon

Source of Support: Nil.

Conflict of Interest: None declared.

Sampling Methods In Reseach: Types, Techniques, & Examples

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.

Learn about our Editorial Process

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.

On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

• Sampling : the process of selecting a representative group from the population under study.
• Target population : the total group of individuals from which the sample might be drawn.
• Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
• Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

• The advantages are that your sample should represent the target population and eliminate sampling bias.
• The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

• The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
• However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

• This is a quick and easy way of choosing participants (advantage)
• It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

• The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

• How it works

Population vs Sample – Definitions, Types & Examples

Published by Alvin Nicolas at September 20th, 2021 , Revised On July 19, 2023

Wondering who wins in the Population vs. Sample battle? Don’t know which one to choose for your survey?

If you are hunting similar questions, congratulations, you have come to the right place.

The Sample and Population sections tend to be a stumbling block for most students, if not all. And if you are one of those people, now is the perfect time to seize an opportunity. This guide contains all the information in the world to sweep through the methodology section of your dissertation proficiently.

Sounds interesting? Let’s get started then!

What is Population in Research?

Population in the research market comprises all the members of a defined group that you generalize to find the results of your study. This means the exact population will always depend on the scope of your respected study. Population in research is not limited to assessing humans; it can be any data parameter, including events, objects, histories, and more possessing a common trait. The measurable quality of the population is called a parameter .

For instance…

If you are to evaluate findings for Health Concerns of Women , you might have to consider all the women in the world that are dead, alive, and will live in the future.

Types of Population

Though there are different types and sub-categories of population, below are the four most common yet important ones to consider.

Countable Population

As the term itself explains, this type of population is one that can be numbered and calculated. It is also known as  finite population . An example of a finite or countable population would be all the students in a college or potential buyers of a brand. A countable population in statistical analysis is thought to be of more benefit than other types.

Uncountable Population

The uncountable population, primarily known as an infinite population, is where the counting units are beyond one’s consideration and capabilities. For instance, the number of rice grains in the field. Or the total number of protons and electrons on a blank page. The fact that this type of population cannot be calculated often leaves room for error and uncertainty.

Hypothetical Population 

This is the population whose unit is not available in a tangible form. Although the population in research analysis includes all sets of possible observations, events, and objects, there still are situations that can only be hypothetical. The perfect example to explain this would be the population of the world. You can give an estimated and hypothetical value gathered by different governments, but can you count all humans existing on the planet? Certainly, no! Another example would be the outcome of rolling dice.

Existent Population

The existent population is the opposite of a hypothetical population, i.e., everything is countable in a concrete form. All the notebooks and pens of students of a particular class could be an example of an existent population.

Is all clear?

Let us move on to the next important term of this guide.

What is Sample in Research?

In quantitative research methodology , the sample is a set of collected data from a defined procedure. It is basically a much smaller part of the whole, i.e., population. The sample depicts all the members of the population that are under observation when conducting research surveys . It can be further assessed to find out about the behavior of the entire population data. The measurable quality of the sample is called a statistic .

Say you send a research questionnaire to all the 200 contacts on your phone, and 42 of them end up filling up the forms. Your sample here is the 42 contacts that participated in the study. The rest of the people who did not participate but were sent invitations become part of your  sampling frame . The sampling frame is the group of people who could possibly be in your research or can be a good fit, which here are the 158 people on your phone.

Can you think of more examples? 

Before we start with the sampling types, here are a few other terminologies related to sampling for a better understanding.

Sample Size : the total number of people selected for the survey/study

Sample Technique : The technique you use in order to get your desired sample size.

Pro Tip: Use a sample for your research when you have a larger population, and you want to generalize your findings for the entire population from this sample.

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Types of Sampling Methods

There are two major types of sampling; Probability Sampling and Non-probability Sampling.

Probability Sampling

In this type of sampling, the researcher tends to set a selection of a few criteria and selects members of a population randomly. This means all the members have an equal chance to be a part of the study.

For example, you are to examine a bag containing rice or some other food item. Now any small portion or part you take for observation will be a true representative of the whole food bag.

It is further divided into the following five types:

• Simple Random Sampling

In this type of probability sampling, the members of the study are chosen by chance or randomly. Wondering if this affects the overall quality of your research? Well, it does not. The fact that every member has an equal chance of being selected, this random selection will do just as fine and speak well for the whole group. The only thing you need to make sure of is that the population is  homogenous , like the bag of rice.

• Systematic Sampling

In systematic sampling, the researcher will select a member after a fixed interval of time. The member selected for the study after this fixed interval is known as the  Kth element.  

For example, if the researcher decides to select a member occurring after every 30 members, the Kth element here would be the 30th element.

• Stratified Random Sampling

If you know the meaning of strata, you might have guessed by now what stratified random sampling is. So, in this type of sampling, the population is first divided into sub-categories. There is no hard and fast rule for it; it is all done randomly.

So, when do we need this kind of sampling?

Stratified random sampling is adopted when the population is not homogenous. It is first divided into groups and categories based on similarities, and later members from each group are randomly selected. The idea is to address the problem of less homogeneity of the population to get a truly representative sample.

• Cluster Sampling

This is where researchers divide the population into clusters that tend to represent the whole population. They are usually divided based on demographic parameters , such as location, age, and sex. It can be a little difficult than the ones earlier mentioned, but cluster sampling is one of the most effective ways to derive interface from the feedback.

For example, suppose the United States government wishes to evaluate the number of people taking the first dose of the COVID-19 vaccine. In that case, they can divide it into groups based on various country estates. Not only will the results be accurate using this sampling method, but it will also be easier for future diagnoses.

• Multi-stage Sampling

Multi-stage sampling is similar to cluster sampling, but let’s say, a complex form of it. In this type of cluster sampling, all the clusters are further divided into sub-clusters. It involves multiple stages, thus the name. Initially, the naturally occurring categories in a population are chosen as clusters, then each cluster is categorized into smaller clusters, and lastly, members are selected from each smaller cluster.

How many stages are enough?

Well, that depends on the nature of your study/research. For some, two to three would be more than enough, while others can take up to 10 rounds or more.

Non-Probability Sampling

Non-probability sampling is the other sampling type where you cannot calculate the probability or chances of any members selected for research. In other words, it is everything the probability sampling is NOT. We just figured out that probability sampling includes selection by chance; this one depends on the subjective judgment of the researcher.

For example, one member might have a 20 percent chance of getting selected in non-probability sampling, while another could have a 60 percent chance.

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Which type of sampling do you think is better?

The debate on this might prevail forever because there is no correct answer for this. Both have their advantages and disadvantages. While non-probability sampling cannot be reliable, it does save your time and costs. Similarly, if probability sampling yields accurate results, it also is not easy to use and sometimes impossible to be conducted, especially when you have a small population at hand.

Types of Non-Probability Sampling

The Four types of non-probability sampling are:

• Convenience Sampling

Convenience sampling relies on the ease of access to specific subjects such as students in the college café or pedestrians on the road. If the researcher can conveniently get the sample for their study, it will fall under this type of sampling. This type of sampling is usually effective when researchers lack time, resources, and money. They have almost zero authority to choose the sample elements and are purely done on immediacy. You send your questionnaire to random contacts on your phone would be convenience sampling as you did not walk extra miles to get the job done.

• Purposive Sampling

Purposive sampling is also known as judgmental sampling because researchers here would effectively consider the study’s purpose and some understanding of what to expect from the target audience. In other words, the target audience is defined here. For instance, if a study is conducted exclusively for Coronavirus patients, all others not affected by the virus will automatically be rejected or excluded from the study.

• Quota Sampling

For quota sampling, you need to have a pre-set standard of sample selection. What happens in quota sampling is that the sample is formed on the basis of specific attributes so that the qualities of this sample can be found in the total population. Slightly complex but worth the hassle.

• Snowball Sampling

Lastly, this type of non-probability sampling is applied when the subjects are rare and difficult to get. For example, if you are to trace and research drug dealers, it would be almost impossible to get them interviewed for the study. This is where snowball sampling comes into play. Similarly, writing a paper on the mental health of rape victims would also be a hard row to hoe. In such a situation, you will only tract a few sources/members and base the rest of your research on it.

To put it briefly, your sample is the group of people participating in the study, while the population is the total number of people to whom the results will apply. As an analogy, if the sample is the garden in your house, the population will be the forests out there.

Now that you have all the details on these two,  can you spot three differences between population and sample ?

Well, we are sure you can give more than just three.

Here are a few differences in case you need a quick revision.

Differences between Population and Sample

This brings us to the end of this guide. We hope you are now clear on these topics and have made up your mind to use a sample for your research or population. The final choice is yours; however, make sure to keep all the above-mentioned facts and particulars in mind and see what works best for you.

Meanwhile, if you have questions and queries or wish to add to this guide, please drop a comment in the comments section below.

FAQs About Population vs. Sample

How can you identify a sample and population.

Sample is the specific group you collect data from, and the population is the entire group you deduce conclusions about. The population is the bigger sample size.

What is a population parameter?

Parameter is some characteristic of the population that cannot be studied directly. It is usually estimated by numbers and figures calculated from the sample data.

Is it better to use a sample instead of a population?

Yes, if you looking for a cost-effective and easier way, a sample is the better option.

What is an example of statistics?

If one office is the sample of the population of all offices in a building, then the average of salaries earned by all employees in the sample office annually would be an example of a statistic .

Does a sample represent the entire population?

Not always. Only a representative sample reflects the entire population of your study. It is an unbiased reflection of what the population is actually like. For instance, you can evaluate the effectiveness by dividing your population on the basis of gender, education, profession, and so on. It depends on how much information is available about your population and the scope of your study. Not to mention how detailed you want your study to be.

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T-distribution describes the probability of data. Although it looks similar to a normal distribution with a bell curve, it has a lower height and a broader curve.

The standard normal distribution is a special kind of normal distribution where the mean is 0, and the standard deviation is 1.

There are a total of four types of data in statistics primarily. They are nominal data, ordinal data, ratio data, and interval data.

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What are Sampling Methods? Techniques, Types, and Examples

Every type of research includes samples from which inferences are drawn. The sample could be biological specimens or a subset of a specific group or population selected for analysis. The goal is often to conclude the entire population based on the characteristics observed in the sample. Now, the question comes to mind: how does one collect the samples? Answer: Using sampling methods. Various sampling strategies are available to researchers to define and collect samples that will form the basis of their research study.

In a study focusing on individuals experiencing anxiety, gathering data from the entire population is practically impossible due to the widespread prevalence of anxiety. Consequently, a sample is carefully selected—a subset of individuals meant to represent (or not in some cases accurately) the demographics of those experiencing anxiety. The study’s outcomes hinge significantly on the chosen sample, emphasizing the critical importance of a thoughtful and precise selection process. The conclusions drawn about the broader population rely heavily on the selected sample’s characteristics and diversity.

Table of Contents

What is sampling?

Sampling involves the strategic selection of individuals or a subset from a population, aiming to derive statistical inferences and predict the characteristics of the entire population. It offers a pragmatic and practical approach to examining the features of the whole population, which would otherwise be difficult to achieve because studying the total population is expensive, time-consuming, and often impossible. Market researchers use various sampling methods to collect samples from a large population to acquire relevant insights. The best sampling strategy for research is determined by criteria such as the purpose of the study, available resources (time and money), and research hypothesis.

For example, if a pet food manufacturer wants to investigate the positive impact of a new cat food on feline growth, studying all the cats in the country is impractical. In such cases, employing an appropriate sampling technique from the extensive dataset allows the researcher to focus on a manageable subset. This enables the researcher to study the growth-promoting effects of the new pet food. This article will delve into the standard sampling methods and explore the situations in which each is most appropriately applied.

What are sampling methods or sampling techniques?

Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. Now that we understand why sampling methods are essential in research, we review the various sample methods in the following sections.

Types of sampling methods  

Before we go into the specifics of each sampling method, it’s vital to understand terms like sample, sample frame, and sample space. In probability theory, the sample space comprises all possible outcomes of a random experiment, while the sample frame is the list or source guiding sample selection in statistical research. The  sample  represents the group of individuals participating in the study, forming the basis for the research findings. Selecting the correct sample is critical to ensuring the validity and reliability of any research; the sample should be representative of the population. 

There are two most common sampling methods: 

• Probability sampling: A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population. 
• Non-probability sampling:  Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population. 

  Irrespective of the research method you opt for, it is essential to explicitly state the chosen sampling technique in the methodology section of your research article. Now, we will explore the different characteristics of both sampling methods, along with various subtypes falling under these categories. 

What is probability sampling?  

The probability sampling method is based on the probability theory, which means that the sample selection criteria involve some random selection. The probability sampling method provides an equal opportunity for all elements or units within the entire sample space to be chosen. While it can be labor-intensive and expensive, the advantage lies in its ability to offer a more accurate representation of the population, thereby enhancing confidence in the inferences drawn in the research.   

Types of probability sampling  

Various probability sampling methods exist, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Here, we provide detailed discussions and illustrative examples for each of these sampling methods: 

• Simple random sampling:  In simple random sampling, each individual has an equal probability of being chosen, and each selection is independent of the others. Because the choice is entirely based on chance, this is also known as the method of chance selection. In the simple random sampling method, the sample frame comprises the entire population. 

For example,  A fitness sports brand is launching a new protein drink and aims to select 20 individuals from a 200-person fitness center to try it. Employing a simple random sampling approach, each of the 200 people is assigned a unique identifier. Of these, 20 individuals are then chosen by generating random numbers between 1 and 200, either manually or through a computer program. Matching these numbers with the individuals creates a randomly selected group of 20 people. This method minimizes sampling bias and ensures a representative subset of the entire population under study. 

• Systematic sampling:  The systematic sampling approach involves selecting units or elements at regular intervals from an ordered list of the population. Because the starting point of this sampling method is chosen at random, it is more convenient than essential random sampling. For a better understanding, consider the following example.  

For example, considering the previous model, individuals at the fitness facility are arranged alphabetically. The manufacturer then initiates the process by randomly selecting a starting point from the first ten positions, let’s say 8. Starting from the 8th position, every tenth person on the list is then chosen (e.g., 8, 18, 28, 38, and so forth) until a sample of 20 individuals is obtained.  

• Stratified sampling: Stratified sampling divides the population into subgroups (strata), and random samples are drawn from each stratum in proportion to its size in the population. Stratified sampling provides improved representation because each subgroup that differs in significant ways is included in the final sample. 

For example, Expanding on the previous simple random sampling example, suppose the manufacturer aims for a more comprehensive representation of genders in a sample of 200 people, consisting of 90 males, 80 females, and 30 others. The manufacturer categorizes the population into three gender strata (Male, Female, and Others). Within each group, random sampling is employed to select nine males, eight females, and three individuals from the others category, resulting in a well-rounded and representative sample of 200 individuals. 

• Clustered sampling: In this sampling method, the population is divided into clusters, and then a random sample of clusters is included in the final sample. Clustered sampling, distinct from stratified sampling, involves subgroups (clusters) that exhibit characteristics similar to the whole sample. In the case of small clusters, all members can be included in the final sample, whereas for larger clusters, individuals within each cluster may be sampled using the sampling above methods. This approach is referred to as multistage sampling. This sampling method is well-suited for large and widely distributed populations; however, there is a potential risk of sample error because ensuring that the sampled clusters truly represent the entire population can be challenging. 

For example, Researchers conducting a nationwide health study can select specific geographic clusters, like cities or regions, instead of trying to survey the entire population individually. Within each chosen cluster, they sample individuals, providing a representative subset without the logistical challenges of attempting a nationwide survey. 

Use s of probability sampling  

Probability sampling methods find widespread use across diverse research disciplines because of their ability to yield representative and unbiased samples. The advantages of employing probability sampling include the following: 

• Representativeness  

Probability sampling assures that every element in the population has a non-zero chance of being included in the sample, ensuring representativeness of the entire population and decreasing research bias to minimal to non-existent levels. The researcher can acquire higher-quality data via probability sampling, increasing confidence in the conclusions. 

• Statistical inference  

Statistical methods, like confidence intervals and hypothesis testing, depend on probability sampling to generalize findings from a sample to the broader population. Probability sampling methods ensure unbiased representation, allowing inferences about the population based on the characteristics of the sample. 

• Precision and reliability  

The use of probability sampling improves the precision and reliability of study results. Because the probability of selecting any single element/individual is known, the chance variations that may occur in non-probability sampling methods are reduced, resulting in more dependable and precise estimations. 

• Generalizability  

Probability sampling enables the researcher to generalize study findings to the entire population from which they were derived. The results produced through probability sampling methods are more likely to be applicable to the larger population, laying the foundation for making broad predictions or recommendations. 

• Minimization of Selection Bias  

By ensuring that each member of the population has an equal chance of being selected in the sample, probability sampling lowers the possibility of selection bias. This reduces the impact of systematic errors that may occur in non-probability sampling methods, where data may be skewed toward a specific demographic due to inadequate representation of each segment of the population. 

What is non-probability sampling?  

Non-probability sampling methods involve selecting individuals based on non-random criteria, often relying on the researcher’s judgment or predefined criteria. While it is easier and more economical, it tends to introduce sampling bias, resulting in weaker inferences compared to probability sampling techniques in research. 

Types of Non-probability Sampling   

Non-probability sampling methods are further classified as convenience sampling, consecutive sampling, quota sampling, purposive or judgmental sampling, and snowball sampling. Let’s explore these types of sampling methods in detail. 

• Convenience sampling:  In convenience sampling, individuals are recruited directly from the population based on the accessibility and proximity to the researcher. It is a simple, inexpensive, and practical method of sample selection, yet convenience sampling suffers from both sampling and selection bias due to a lack of appropriate population representation. 

For example, imagine you’re a researcher investigating smartphone usage patterns in your city. The most convenient way to select participants is by approaching people in a shopping mall on a weekday afternoon. However, this convenience sampling method may not be an accurate representation of the city’s overall smartphone usage patterns as the sample is limited to individuals present at the mall during weekdays, excluding those who visit on other days or never visit the mall.

• Consecutive sampling: Participants in consecutive sampling (or sequential sampling) are chosen based on their availability and desire to participate in the study as they become available. This strategy entails sequentially recruiting individuals who fulfill the researcher’s requirements. 

For example, In researching the prevalence of stroke in a hospital, instead of randomly selecting patients from the entire population, the researcher can opt to include all eligible patients admitted over three months. Participants are then consecutively recruited upon admission during that timeframe, forming the study sample. 

• Quota sampling:  The selection of individuals in quota sampling is based on non-random selection criteria in which only participants with certain traits or proportions that are representative of the population are included. Quota sampling involves setting predetermined quotas for specific subgroups based on key demographics or other relevant characteristics. This sampling method employs dividing the population into mutually exclusive subgroups and then selecting sample units until the set quota is reached.  

For example, In a survey on a college campus to assess student interest in a new policy, the researcher should establish quotas aligned with the distribution of student majors, ensuring representation from various academic disciplines. If the campus has 20% biology majors, 30% engineering majors, 20% business majors, and 30% liberal arts majors, participants should be recruited to mirror these proportions. 

• Purposive or judgmental sampling: In purposive sampling, the researcher leverages expertise to select a sample relevant to the study’s specific questions. This sampling method is commonly applied in qualitative research, mainly when aiming to understand a particular phenomenon, and is suitable for smaller population sizes. 

For example, imagine a researcher who wants to study public policy issues for a focus group. The researcher might purposely select participants with expertise in economics, law, and public administration to take advantage of their knowledge and ensure a depth of understanding.  

• Snowball sampling:  This sampling method is used when accessing the population is challenging. It involves collecting the sample through a chain-referral process, where each recruited candidate aids in finding others. These candidates share common traits, representing the targeted population. This method is often used in qualitative research, particularly when studying phenomena related to stigmatized or hidden populations. 

For example, In a study focusing on understanding the experiences and challenges of individuals in hidden or stigmatized communities (e.g., LGBTQ+ individuals in specific cultural contexts), the snowball sampling technique can be employed. The researcher initiates contact with one community member, who then assists in identifying additional candidates until the desired sample size is achieved.

Uses of non-probability sampling  

Non-probability sampling approaches are employed in qualitative or exploratory research where the goal is to investigate underlying population traits rather than generalizability. Non-probability sampling methods are also helpful for the following purposes: 

• Generating a hypothesis  

In the initial stages of exploratory research, non-probability methods such as purposive or convenience allow researchers to quickly gather information and generate hypothesis that helps build a future research plan.  

• Qualitative research  

Qualitative research is usually focused on understanding the depth and complexity of human experiences, behaviors, and perspectives. Non-probability methods like purposive or snowball sampling are commonly used to select participants with specific traits that are relevant to the research question.  

• Convenience and pragmatism  

Non-probability sampling methods are valuable when resource and time are limited or when preliminary data is required to test the pilot study. For example, conducting a survey at a local shopping mall to gather opinions on a consumer product due to the ease of access to potential participants.  

Probability vs Non-probability Sampling Methods  

Frequently asked questions  

• What is multistage sampling ? Multistage sampling is a form of probability sampling approach that involves the progressive selection of samples in stages, going from larger clusters to a small number of participants, making it suited for large-scale research with enormous population lists.  
• What are the methods of probability sampling? Probability sampling methods are simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling.
• How to decide which type of sampling method to use? Choose a sampling method based on the goals, population, and resources. Probability for statistics and non-probability for efficiency or qualitative insights can be considered . Also, consider the population characteristics, size, and alignment with study objectives.
• What are the methods of non-probability sampling? Non-probability sampling methods are convenience sampling, consecutive sampling, purposive sampling, snowball sampling, and quota sampling.
• Why are sampling methods used in research? Sampling methods in research are employed to efficiently gather representative data from a subset of a larger population, enabling valid conclusions and generalizations while minimizing costs and time.  

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Sampling is the statistical process of selecting a subset—called a ‘sample’—of a population of interest for the purpose of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviours within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalised back to the population of interest. Improper and biased sampling is the primary reason for the often divergent and erroneous inferences reported in opinion polls and exit polls conducted by different polling groups such as CNN/Gallup Poll, ABC, and CBS, prior to every US Presidential election.

The sampling process

As Figure 8.1 shows, the sampling process comprises of several stages. The first stage is defining the target population. A population can be defined as all people or items ( unit of analysis ) with the characteristics that one wishes to study. The unit of analysis may be a person, group, organisation, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious. For example, if a manufacturer wants to determine whether finished goods manufactured at a production line meet certain quality requirements or must be scrapped and reworked, then the population consists of the entire set of finished goods manufactured at that production facility. At other times, the target population may be a little harder to understand. If you wish to identify the primary drivers of academic learning among high school students, then what is your target population: high school students, their teachers, school principals, or parents? The right answer in this case is high school students, because you are interested in their performance, not the performance of their teachers, parents, or schools. Likewise, if you wish to analyse the behaviour of roulette wheels to identify biased wheels, your population of interest is not different observations from a single roulette wheel, but different roulette wheels (i.e., their behaviour over an infinite set of wheels).

The second step in the sampling process is to choose a sampling frame . This is an accessible section of the target population—usually a list with contact information—from where a sample can be drawn. If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study. If your target population is organisations, then the Fortune 500 list of firms or the Standard & Poor’s (S&P) list of firms registered with the New York Stock exchange may be acceptable sampling frames.

Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalisable to the population. For instance, if your target population is organisational employees at large (e.g., you wish to study employee self-esteem in this population) and your sampling frame is employees at automotive companies in the American Midwest, findings from such groups may not even be generalisable to the American workforce at large, let alone the global workplace. This is because the American auto industry has been under severe competitive pressures for the last 50 years and has seen numerous episodes of reorganisation and downsizing, possibly resulting in low employee morale and self-esteem. Furthermore, the majority of the American workforce is employed in service industries or in small businesses, and not in automotive industry. Hence, a sample of American auto industry employees is not particularly representative of the American workforce. Likewise, the Fortune 500 list includes the 500 largest American enterprises, which is not representative of all American firms, most of which are medium or small sized firms rather than large firms, and is therefore, a biased sampling frame. In contrast, the S&P list will allow you to select large, medium, and/or small companies, depending on whether you use the S&P LargeCap, MidCap, or SmallCap lists, but includes publicly traded firms (and not private firms) and is hence still biased. Also note that the population from which a sample is drawn may not necessarily be the same as the population about which we actually want information. For example, if a researcher wants to examine the success rate of a new ‘quit smoking’ program, then the target population is the universe of smokers who had access to this program, which may be an unknown population. Hence, the researcher may sample patients arriving at a local medical facility for smoking cessation treatment, some of whom may not have had exposure to this particular ‘quit smoking’ program, in which case, the sampling frame does not correspond to the population of interest.

The last step in sampling is choosing a sample from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if generalisability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.

Probability sampling

Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Sample statistics thus produced, such as sample mean or standard deviation, are unbiased estimates of population parameters, as long as the sampled units are weighted according to their probability of selection. All probability sampling have two attributes in common: every unit in the population has a known non-zero probability of being sampled, and the sampling procedure involves random selection at some point. The different types of probability sampling techniques include:

Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called ‘strata’), and a simple random sample is drawn within each subgroup. In the previous example of selecting 200 firms from a list of 1,000 firms, you can start by categorising the firms based on their size as large (more than 500 employees), medium (between 50 and 500 employees), and small (less than 50 employees). You can then randomly select 67 firms from each subgroup to make up your sample of 200 firms. However, since there are many more small firms in a sampling frame than large firms, having an equal number of small, medium, and large firms will make the sample less representative of the population (i.e., biased in favour of large firms that are fewer in number in the target population). This is called non-proportional stratified sampling because the proportion of the sample within each subgroup does not reflect the proportions in the sampling frame—or the population of interest—and the smaller subgroup (large-sized firms) is oversampled . An alternative technique will be to select subgroup samples in proportion to their size in the population. For instance, if there are 100 large firms, 300 mid-sized firms, and 600 small firms, you can sample 20 firms from the ‘large’ group, 60 from the ‘medium’ group and 120 from the ‘small’ group. In this case, the proportional distribution of firms in the population is retained in the sample, and hence this technique is called proportional stratified sampling. Note that the non-proportional approach is particularly effective in representing small subgroups, such as large-sized firms, and is not necessarily less representative of the population compared to the proportional approach, as long as the findings of the non-proportional approach are weighted in accordance to a subgroup’s proportion in the overall population.

Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into ‘clusters’—usually along geographic boundaries—randomly sample a few clusters, and measure all units within that cluster. For instance, if you wish to sample city governments in the state of New York, rather than travel all over the state to interview key city officials (as you may have to do with a simple random sample), you can cluster these governments based on their counties, randomly select a set of three counties, and then interview officials from every office in those counties. However, depending on between-cluster differences, the variability of sample estimates in a cluster sample will generally be higher than that of a simple random sample, and hence the results are less generalisable to the population than those obtained from simple random samples.

Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion. For instance, why are some firms consistently more profitable than other firms? To conduct such a study, you would have to categorise a sampling frame of firms into ‘high profitable’ firms and ‘low profitable firms’ based on gross margins, earnings per share, or some other measure of profitability. You would then select a simple random sample of firms in one subgroup, and match each firm in this group with a firm in the second subgroup, based on its size, industry segment, and/or other matching criteria. Now, you have two matched samples of high-profitability and low-profitability firms that you can study in greater detail. Matched-pairs sampling techniques are often an ideal way of understanding bipolar differences between different subgroups within a given population.

Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling. For instance, you can stratify a list of businesses based on firm size, and then conduct systematic sampling within each stratum. This is a two-stage combination of stratified and systematic sampling. Likewise, you can start with a cluster of school districts in the state of New York, and within each cluster, select a simple random sample of schools. Within each school, you can select a simple random sample of grade levels, and within each grade level, you can select a simple random sample of students for study. In this case, you have a four-stage sampling process consisting of cluster and simple random sampling.

Non-probability sampling

Non-probability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, non-probability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalised back to the population. Types of non-probability sampling techniques include:

Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping centre and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centres. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping centre such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping centre close to a university will attract primarily university students with unique purchasing habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalisability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalisable inferences.

Quota sampling. In this technique, the population is segmented into mutually exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota. In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70 per cent Caucasians, 15 per cent Hispanic-Americans, and 13 per cent African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping centre and ask people their voting preferences. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup (or African-Americans when you have 13 responses) even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population.

Non-proportional quota sampling is less restrictive in that you do not have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African-Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping centre in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population, but may be useful in that it allows capturing the opinions of small and under-represented groups through oversampling.

Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied. For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample a group of corporate accountants who are familiar with this Act. The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalisable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also work in network administration. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

Statistics of sampling

In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias. In this section, we will try to understand what these terms mean and how they are related to each other.

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a ‘statistic’ is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not ‘statistic’ because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population. The difference between the two is called sampling error . Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to the corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown in Figure 8.3, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical—and equal to the population parameter—and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples , while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample . Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error . In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Statistics By Jim

Making statistics intuitive

Sampling Methods: Different Types in Research

By Jim Frost 3 Comments

What Are Sampling Methods?

Sampling methods are the processes by which you draw a sample from a population . When performing research, you’re typically interested in the results for an entire population. Unfortunately, they are almost always too large to study fully. Consequently, researchers use samples to draw conclusions about a population—the process of making statistical inferences.

A population is the complete set of individuals that you’re studying. A sample is the subset of the population that you actually measure, test, or evaluate and base your results. Sampling methods are how you obtain your sample.

Before beginning your study, carefully define the population because your results apply to the target population. You can define your population as narrowly as necessary to meet the needs of your study—for example, adult Swedish women who are otherwise healthy but have osteoporosis. Then choose your sampling method.

Learn more about populations and samples , inferential vs. descriptive statistics and populations and parameters .

In research and inferential statistics , sampling methods are a vital issue. How you draw your sample affects how much you can trust the results! If your sample doesn’t reflect the population, your results might not be valid. It’s a crucial part of experimental design .

In this post, learn more about sampling methods, which ones produce representative samples, and the pros and cons of each procedure.

Probability vs Non-Probability Sampling Methods

Sampling methods have the following two broad categories:

• Probability sampling : Entails random selection and typically, but not always, requires a list of the entire population.
• Non-probability sampling : Does not use random selection but some other process, such as convenience. Usually does not sample from the whole population.

Probability sampling is typically more difficult and costly to implement, but, in exchange, these processes tend to increase validity by producing representative samples. In short, you can make valid conclusions about the population.  A statistical inference is when you use a sample to learn about a population. Learn more about Making Statistical Inferences .

On the other hand, non-probability sampling methods are often easier and less expensive, but the trade-off is that the validity of your conclusions is questionable. You might not be able to trust the results. Sampling bias is more likely to occur.

Learn more about Validity in Research and Psychology: Types & Examples and Internal and External Validity .

Probability Sampling Methods

Given the benefits of using representative samples, you’ll typically want to use a probability sampling method whenever possible. Let’s go over the standard methods. They each have pros and cons. Click the links to learn more about each sampling method and see examples. Learn more about representative samples .

To use a probability method, you’ll first need to develop a sampling frame, which lists all members of your target population. Then you can use one of the following methods.

Learn more about Sampling Frames: Definition, Examples & Uses .

Simple Random Sampling (SRS)

In simple random sampling (SRS), researchers take a complete list of the population and randomly select participants from it. All population members have an equal likelihood of being selected. Out of all sampling methods, statisticians consider this one to be the gold standard for producing representative samples. It’s entirely random, leaving little room for accidentally biasing the results.

However, this sampling method has some drawbacks.

First and foremost, this method can be pretty unwieldy and require abundant resources. For one thing, it requires a list of all population members, which can be a tremendous hurdle by itself. Attempting to perform SRS with an incomplete population list causes undercoverage bias and a nonrepresentative sample.

Furthermore, while random selection is beneficial, it also ensures that the subjects are maximally dispersed, making them harder to contact.

SRS can exclude smaller but crucial subpopulations purely by chance. Additionally, this approach produces less precise estimates for subgroups and the differences between subgroups than some other probability sampling methods.

Learn more about Simple Random Sampling  and Undercoverage Bias: Definition & Examples

Systematic Sampling

Systematic sampling is similar to SRS but attempts to ease some of the difficulties for researchers. There are several versions of this method.

One form uses a complete list of the population. The researchers randomly select the first subject and then move down the list choosing every X th subject rather than using a randomized technique.

The other form does not use a complete list of the population. This sampling method is suitable for populations that are tough to document, such as the homeless, because a comprehensive list won’t exist. The essential requirement for this sampling method is knowing how to locate them. While it’s not perfect, it’s a feasible option when you can’t obtain the full list.

Suppose you want to survey theater patrons but lack a complete list. Instead, you can use systematic sampling and recruit every 20th person who exits the theater. This approach works because they leave randomly.

This sampling method has some disadvantages. The form that uses a complete list of the population can closely mirror the results of simple random sampling. However, the non-randomness increases the potential for manipulation, even if accidentally. Additionally, patterns in the list can unintentionally create a non-representative sample.

The form that doesn’t use a list has more potential problems. Namely, it increases the potential for missing subgroups and acquiring a non-representative sample. This sampling method increases the knowledge you must have about the population and their habits. Without that knowledge, you won’t be able to find subjects that reflect the whole population.

Learn more about Systematic Sampling .

Stratified Sampling

In stratified sampling, researchers divide a population into similar subpopulations (strata). Then they randomly sample from the strata.

This sampling method can guarantee the presence of small but vital subpopulations in the sample. Relative to SRS, this method can increase the precision of subgroup estimates and the differences between subgroups. In short, it helps researchers gain a better understanding of the subgroups. Dividing the whole population into smaller, more similar subsets can also reduce costs and simplify data collection.

The drawbacks are that this sampling method requires additional upfront knowledge and planning. The researchers must know enough about the subgroups to devise an effective strata scheme. Then they must have sufficient information about all population members to assign them to the correct strata.

Learn more about Stratified Sampling .

Cluster Sampling

Like stratified sampling, the cluster sampling method divides the whole population into smaller groups. However, unlike strata, each cluster mirrors the full diversity present in the population. Then the researchers randomly sample from some of these clusters.

The primary benefit of this sampling method is that it reduces the costs of studying large, geographically dispersed populations. Using this method, researchers don’t need to sample the entire geographic region but only certain areas because they know individual clusters are similar to the population. Additionally, they don’t need to develop a list of potential subjects for clusters from which they’re not sampling. These considerations can significantly reduce planning, administrative, and travel costs.

When researchers can’t create a list of the entire population, cluster sampling can be an excellent choice.

On the downside, cluster sampling increases the design complexity. Researchers must understand how well each cluster approximates the whole population. If the clusters don’t fully represent the population, results can be biased. In real-world studies, clusters tend to be naturally occurring groups that don’t mirror the population, which reduces the ability to draw valid conclusions.

Learn more about Cluster Sampling .

Non-Probability Sampling Methods

Non-probability sampling methods don’t use random selection, and they typically don’t use a complete population list. While these methods are simpler and less expensive, your results are more likely to be biased, reducing your ability to make sound conclusions.

Researchers often use non-probability sampling methods for exploratory research, pilot studies, and qualitative research . These sampling methods provide quick and rough assessments, help work kinks out of measurement instruments and procedures, and help refine the design for a more rigorous study in the future.

Below are several standard non-probability sampling methods:

• Convenience sampling : The main criteria for recruiting subjects are those who are easy to contact and willing to participate. There are no inclusion requirements. Online polls are a type of convenience sampling. Learn more about Convenience Sampling .
• Quota Sampling : Non-random selection of subjects from population subgroups that the researchers define. Learn more about Quota Sampling .
• Purposive sampling : Investigators use subject-area knowledge to handpick a sample they think will help their study. Learn more about Purposive Sampling .
• Snowball sampling : Researchers use subjects to find and recruit other subjects. This method is helpful when a population is hard to contact. When recruits help you find more recruits, and those help find even more, and so on, the total number snowballs. Learn more about Snowball Sampling .

As you can see, there are many sampling methods. Each one has benefits and disadvantages. When designing a study, evaluate the nature of your target population, your research goals, and the available time and resources to choose your sampling method. After deciding between the sampling methods, calculate your sample size using a power analysis .

Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards (nih.gov)

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Reader Interactions

July 24, 2024 at 8:56 am

Hello Mr. Frost,

I would like to know whether people with mild Parkinson’s Disease symptoms are less likely to have kidney stones. Do PwP (People with Parkinson’s) have significantly less incidences of kidney stones than in the general population (~ 10%). So far, I have asked 12 people I know who has been diagnosed with Parkinson’s and 0% had kidney stones. I would like to increase my sampling size by randomly sampling members of a forum for PwP I belong to. Should I get a list of all forum subscribers and randomly select around 40 forum members to pose the question, “If you have been officially diagnosed with Parkinson’s, have also had a kidney stone?”. What would you suggest? I had posed the question in the forum before, but only PwP folks that had a Kidney stone responded.

Thanks, Mike

May 17, 2022 at 12:38 am

I think stratified sampling will work __ mke two groups as stratas _ then use SRS to obtain a complete sample .

May 15, 2022 at 7:37 pm

hi.what sampling technique will i use if my respondents are 1st yr college students awardees vs non awardees of different courses?

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• Population vs Sample | Definitions, Differences & Examples

Population vs Sample | Definitions, Differences & Examples

Published on 3 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organisations, countries, species, or organisms.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs sample statistic, practice questions: populations vs samples, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalised and low-income groups have been difficult to contact, locate, and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods . Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalise findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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Bhandari, P. (2022, December 05). Population vs Sample | Definitions, Differences & Examples. Scribbr. Retrieved 12 August 2024, from https://www.scribbr.co.uk/research-methods/population-versus-sample/

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• Key Differences

Know the Differences & Comparisons

Difference Between Population and Sample

Population represents the entirety of persons, units, objects and anything that is capable of being conceived, having certain properties. On the contrary, the sample is a finite subset of the population, that is chosen by a systematic process, to find out the characteristics of the parent set. The article presented below describes the differences between population and sample.

Content: Population Vs Sample

Comparison chart.

Definition of Population

In simple terms, population means the aggregate of all elements under study having one or more common characteristic, for example, all people living in India constitutes the population. The population is not confined to people only, but it may also include animals, events, objects, buildings, etc. It can be of any size, and the number of elements or members in a population is known as population size, i.e. if there are hundred million people in India, then the population size (N) is 100 million. The different types of population are discussed as under:

• Finite Population : When the number of elements of the population is fixed and thus making it possible to enumerate it in totality, the population is said to be finite.
• Infinite Population : When the number of units in a population are uncountable, and so it is impossible to observe all the items of the universe, then the population is considered as infinite.
• Existent Population : The population which comprises of objects that exist in reality is called existent population.
• Hypothetical Population : Hypothetical or imaginary population is the population which exists hypothetically.
• The population of all workers working in the sugar factory.
• The population of motorcycles produced by a particular company.
• The population of mosquitoes in a town.
• The population of tax payers in India.

Definition of Sample

By the term sample, we mean a part of population chosen at random for participation in the study. The sample so selected should be such that it represent the population in all its characteristics, and it should be free from bias, so as to produce miniature cross-section, as the sample observations are used to make generalisations about the population.

In other words, the respondents selected out of population constitutes a ‘sample’, and the process of selecting respondents is known as ‘sampling.’ The units under study are called sampling units, and the number of units in a sample is called sample size.

While conducting statistical testing, samples are mainly used when the sample size is too large to include all the members of the population under study.

Key Differences Between Population and Sample

The difference between population and sample can be drawn clearly on the following grounds:

• The collection of all elements possessing common characteristics that comprise universe is known as the population. A subgroup of the members of population chosen for participation in the study is called sample.
• The population consists of each and every element of the entire group. On the other hand, only a handful of items of the population is included in a sample.
• The characteristic of population based on all units is called parameter while the measure of sample observation is called statistic.
• When information is collected from all units of population, the process is known as census or complete enumeration. Conversely, the sample survey is conducted to gather information from the sample using sampling method.
• With population, the focus is to identify the characteristics of the elements whereas in the case of the sample; the focus is made on making the generalisation about the characteristics of the population, from which the sample came from.

In spite of the above differences, it is also true that sample and population are related to each other, i.e. sample is drawn from the population, so without population sample may not exist. Further, the primary objective of the sample is to make statistical inferences about the population, and that too would be as accurate as possible. The greater the size of the sample, the higher is the level of accuracy of generalisation.

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Michael says

March 9, 2019 at 8:11 am

Quite definitive and simple to understand

March 23, 2019 at 1:18 pm

Thanks a lot.

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September 24, 2023 at 9:14 pm

thanks very much

September 24, 2023 at 6:23 pm

Perfect thank you alot, this is exactly what I was looking for

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Data Collection Methods

Population vs sample in research: What’s the difference?

Population and sample are two important terms in research. Having a thorough understanding of these terms is important if you want to conduct effective research — and that’s especially true for new researchers. If you need a primer on population vs sample, this article covers everything you need to know, including how to collect data from either group.

What is a population?

Outside the research field, population refers to the number of people living in a place at a particular time. In research, however, a population is a well-defined group of people or items that share the same characteristics. It’s the group that a researcher is interested in studying.

Arvind Sharma , an assistant professor at Boston College, explains that a population isn’t limited to people: “It can be any unit from which you obtain data to carry out your research.” This group could consist of humans, animals, or objects.

Below are some examples of population:

• Male adults in the United States
• World Cup football matches
• Insects in American rainforests

As you can see from the examples above, populations are usually large, so it’s often difficult to survey an entire population. That’s where sampling comes in.

What is a sample?

A sample is a select group of individuals from the research population. A sample is only a subset or a subgroup of the population and, by definition, is always smaller than the population. However, well-selected samples accurately represent the entire population.

Below are some examples to illustrate the differences between population vs sample:

The sample a researcher choses from any population will depend on their research goals and objectives. For example, if you’re researching employees in a large corporation, you may be interested in C-level executives, junior-level employees, or even external contractors.

What are the differences between population and sample?

Below are the main differences between a population and a sample, as pointed out by Sharma:

What are some reasons for sampling?

Collecting data from an entire population isn’t always possible. “In fact,” explains Sharma, “99 percent of the time, we can’t survey the entire population. Other times, it is not even necessary.

“A representative sample drawn using appropriate sampling techniques will provide results that are representative of the entire population. So, it would be unnecessary to survey every member of the population.”

Below are the other most important reasons for using sampling.

Population studies are more expensive than sample surveys. For example, researching the entire population of adult male Americans would be too costly. It’s more cost-effective to work with a representative sample.

2. Practicality

Consider the adult male American research example. Even if a researcher had the resources to survey all the males in that population, it may be difficult or impossible to obtain responses from all participants. For example, the researchers may not even be able to contact all members of this population.

3. Manageability

It’s easier to manage time, costs, and resources when working with samples. Also, it’s easier to manage the data you collect from a sample vs a population. For example, it’s easier to analyze data from a sample of 1,000 adult males than a sample of all adult males in the U.S. or even a specific state.

How can you collect data from a population?

Collecting data from an entire population requires a census. A census is a collection of information from all sections of the population. It’s a complete enumeration of the population, and it requires considerable resources, which is why researchers often work with a sample.

If the target population is small, however, then you can collect data from every member of the population. For example, you can survey the performance of the members of the customer service team in a bank branch. The number is likely to be more manageable, so you can access and collect data from this population.

What methods can you use to collect data from a sample?

There are so many approaches for collecting data from samples. Some of the more commonly used methods are listed below.

1. Simple random sampling

In simple random sampling, researchers select individuals at random from the population. In this method, every member of the population has an equal chance of being selected.

For example, suppose you want to select a sample of 50 employees from a population of 500 employees. You could write down all the names of the employees, place them in a hat or container, and pick employee names at random like you would in a lottery. That’s an example of simple random sampling. It works best when the population isn’t too large.

2. Systematic sampling

This is a sampling technique that selects every k th item from the population. It’s a type of probability sampling researchers use to select items from a population randomly. A researcher may want to use this technique if they’re working with a large population and need to sample only a small number of items in order to study them in detail.

For example, to apply systematic sampling in a performance survey of 1,000 customer service team members, we can choose every fifth member — i.e., the fifth, 10th, 15th customer service rep, and so on.

For more details on  what is systematic sampling , check out our guide

3. Stratified sampling

In this probability sampling method, researchers divide members of the population into groups based on age, race, ethnicity, or sex. Researchers select individuals randomly from those groups to form a sample. This ensures that every group is equally represented.

What is a sampling error?

A sampling error is the difference between the value obtained from a sample and the true population value. It’s the difference between an estimate from a sample and the true population value.

A sampling error can occur if you don’t have enough people in your sample or if you select people who aren’t representative of the population. This can impact the accuracy of your survey. For example, if you want to know what percentage of adults are vegetarian but only ask vegetarians in a specific city, then this would be an example of selecting people who aren’t representative of the population.

According to Sharma, you can reduce sampling errors by increasing the sample size . He also notes that sample design and variation within a population affect sampling errors.

How can Jotform make the research process easier?

Whether you’re surveying a small or large sample or even an entire population, Jotform gives you the right tools to make your research easier. With Jotform’s free online survey maker, you can create engaging surveys and collect responses online. You can easily customize any of our 10,000-plus free survey templates to suit your research purposes. Get started with Jotform today.

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Chapter 7: Sampling Techniques

7.2 Population versus Samples

If you had all the money and resources in the world, you could potentially sample the whole population. However, money and resources usually limit sampling, and furthermore all members of a population may not actually be identifiable in a way that allows you to sample. As a result, researchers take a sample, or a subgroup of people (or objects) from the population and study that instead of the population. In social scientific research, the population is the cluster of people, events, things, or other phenomena in which you are most interested. It is often the “who” or “what” that you want to be able to say something about at the end of your study. Populations in research may be rather large, such as “the Canadian people,” but typically they are more focused than that. For example, a large study, for which the population of interest really is the Canadian people, will likely specify which Canadian people, such as adults over the age of 18 or citizens or legal residents.

One of the most surprising and often frustrating lessons students of research methods learn is that there is a difference between one’s population of interest and one’s study sample. While there are certainly exceptions, more often than not, a researcher’s population and the sample are not the same. A sample is the cluster of people or events, for example, from or about which you will actually gather data. Some sampling strategies allow researchers to make claims about populations that are much larger than their actual sample with a fair amount of confidence. Other sampling strategies are designed to allow researchers to make theoretical contributions rather than to make sweeping claims about large populations. We will discuss both types of strategies later in this chapter.

As mentioned previously, it is quite rare for a researcher to gather data from their entire population of interest. This might sound surprising or disappointing until you think about the kinds of research questions that sociologists typically ask. For example, suppose we wish to answer the following research question: “How do men’s and women’s college experiences differ, and how are they similar?” Would you expect to be able to collect data from all college students across all nations from all historical time periods? Unless you plan to make answering this research question your entire life’s work (and then some), the answer is probably “no.” So then, what is a researcher to do? Does not having the time or resources to gather data from every single person of interest mean having to give up your research interest? Absolutely not. It just means having to make some hard choices about sampling, and then being honest with yourself and your readers about the limitations of your study based on the sample from whom you were able to actually collect data. This resource can help you better understand how to get from the theoretical population (to whom you want to generalize) to your sample (who will actually be in your study) .

Now having said this, there are certainly times when it is possible to access every member of the population. This happens when the population is small, accessible, and willing to participate, or the researcher has access to relevant records. For example, suppose that a university dean wants to analyse the final graduating scores for all students enrolled in the university’s health sciences program, for 2015 to 2019. The dean wants to know if there is a trend toward an average increase in final graduating scores in health sciences, over this time period, as she suspects. Since the dean is only interested in her particular university and only those students who graduated from health sciences from 2015 to 2019, she can easily use the whole population. In this case, the population is the records of final graduating scores for all students enrolled in the university’s health sciences program from 2015 to 2019.

To summarize, we use sampling when the population is large and we simply do not have the time, financial support, and/or ability (i.e. lack of laboratory equipment) to reach the entire population.

In Table 7.1 you will find some examples of a population versus a sample, and the type of research methodology that might lead such a study. Do not worry about the methodology column now, as you have most likely not yet read the applicable chapters. Make a note to yourself and return to this table after reading Chapters 8 through 13.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Population Sampling Techniques

Population sampling is the process of taking a subset of subjects that is representative of the entire population. The sample must have sufficient size to warrant statistical analysis.

This article is a part of the guide:

• Non-Probability Sampling
• Convenience Sampling
• Random Sampling
• Stratified Sampling
• Research Population

Browse Full Outline

• 1 What is Sampling?
• 2.1 Sample Group
• 2.2 Research Population
• 2.3 Sample Size
• 2.4 Randomization
• 3.1 Statistical Sampling
• 3.2 Sampling Distribution
• 3.3.1 Random Sampling Error
• 4.1 Random Sampling
• 4.2 Stratified Sampling
• 4.3 Systematic Sampling
• 4.4 Cluster Sampling
• 4.5 Disproportional Sampling
• 5.1 Convenience Sampling
• 5.2 Sequential Sampling
• 5.3 Quota Sampling
• 5.4 Judgmental Sampling
• 5.5 Snowball Sampling

Sampling is done usually because it is impossible to test every single individual in the population. It is also done to save time, money and effort while conducting the research.

Still, every researcher must keep in mind that the ideal scenario is to test all the individuals to obtain reliable, valid and accurate results. If testing all the individuals is impossible, that is the only time we rely on sampling techniques.

Performing population sampling must be conducted correctly since errors can lead to inaccurate and misleading data.

Types of Sampling

In this type of population sampling, members of the population do not have equal chance of being selected. Due to this, it is not safe to assume that the sample fully represents the target population. It is also possible that the researcher deliberately chose the individuals that will participate in the study.

Non-probability population sampling method is useful for pilot studies , case studies , qualitative research , and for hypothesis development .

This sampling method is usually employed in studies that are not interested in the parameters of the entire population. Some researchers prefer this sampling technique because it is cheap, quick and easy.

Probability Sampling

In probability sampling, every individual in the population have equal chance of being selected as a subject for the research.

This method guarantees that the selection process is completely randomized and without bias .

The most basic example of probability sampling is listing all the names of the individuals in the population in separate pieces of paper, and then drawing a number of papers one by one from the complete collection of names.

The advantage of using probability sampling is the accuracy of the statistical methods after the experiment. It can also be used to estimate the population parameters since it is representative of the entire population. It is also a reliable method to eliminate sampling bias.

Steps in Recruiting the Appropriate Research Sample

In research , population is a precise group of people or objects that possesses the characteristic that is questioned in a study. To be able to clearly define the target population, the researcher must identify all the specific qualities that are common to all the people or objects in focus.

A population can be as simple as all the citizens of California or it can be specific like all male 17-year old high school students with asthma who have been taking bronchodilators since 12 years of age.

This process will help the researchers grasp a concrete idea pertaining to the sample that they can obtain from the population.

If the researcher has plenty of time, funds and workforce, he can opt to conduct the study using a completely randomized sample but if the time money and workforce is limited, the researcher can opt to use convenience sampling .

But still, the type of population sampling must depend on the research question and design.

• Allocate the available money, time and workforce for recruitment.

Research Triad

Result Generalization

Results from the sample can be generalized to speak for the entire population from which the aforementioned sample was taken.

Population Sampling

The resulting sample must be representative of the population to warrant accurate generalization.

Experimentation/testing

Should be systematic, repeatable and nonbiased .

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What Is a Simple Random Sample?

• How It Works
• Conducting a Simple Random Sample

Random Sampling Techniques

• Simple Random vs. Other Methods
• Pros and Cons
• Simple Random Sample FAQs

The Bottom Line

• Corporate Finance
• Financial Analysis

Simple Random Sampling: 6 Basic Steps With Examples

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group.

Key Takeaways

• A simple random sample takes a small, random portion of the entire population to represent the entire data set, where each member has an equal probability of being chosen.
• Researchers can create a simple random sample using methods such as lotteries or random draws.
• A sampling error can occur with a simple random sample if the sample does not end up accurately reflecting the population it is supposed to represent.
• Simple random samples are determined by assigning sequential values to each item within a population, then randomly selecting those values.
• Systematic sampling, stratified sampling, and cluster sampling are other types of sampling approaches that may be used instead of simple random sampling.

Investopedia / Madelyn Goodnight

Understanding a Simple Random Sample

Researchers can create a simple random sample using a couple of methods. With a lottery method, each member of the population is assigned a number, and numbers are then selected at random.

An example of a simple random sample would be to choose the names of 25 employees out of a hat from a company of 250 employees. In this case the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen. Random sampling is used in science to conduct randomized control tests or for blinded experiments.

The example in which the names of 25 employees out of 250 are chosen out of a hat is an example of the lottery method at work. Each of the 250 employees would be assigned a number between one and 250, after which 25 of those numbers would be chosen at random.

Because individuals who make up the subset of the larger group are chosen at random, each individual in the large population set has the same probability of being selected. In most cases this creates a balanced subset that carries the greatest potential for representing the larger group as a whole.

A manual lottery method can be quite onerous for larger populations. Selecting a random sample from a large population usually requires a computer-generated process. The same methodology as the lottery method is used, only the number assignments and subsequent selections are performed by computers, not humans.

Room for Error

With a simple random sample, there has to be room for error represented by a plus and minus variance ( sampling error ). For example, if a survey is taken to determine how many students are left-handed in a high school of 1,000 students, random sampling can determine that eight out of the 100 sampled are left-handed. The conclusion would then be that 8% of the student population of the high school are left-handed, when in fact the global average would be closer to 10%.

The same is true regardless of the subject matter. A survey on the percentage of the student population that has green eyes or a physical disability would result in a mathematical probability based on a simple random survey, but always with a plus or minus variance. The only way to have 100% accuracy rate would be to survey all 1,000 students which, while possible, would be impractical.

Although simple random sampling is intended to be an unbiased approach to surveying, sample selection bias can occur. When a sample set of the larger population is not inclusive enough, representation of the full population is skewed and requires additional sampling techniques.

How to Conduct a Simple Random Sample

The simple random sampling process entails six steps, each performed in sequential order.

Step 1: Define the Population

The starting point of statistical analysis is to determine the population base. This is the group about which you wish to learn more, confirm a hypothesis , or determine a statistical outcome. This step is simply to identify what that population base is and ensure that the group will adequately cover the outcome you are trying to ascertain.

Example: You want to learn how the stocks of the largest companies in the United States have performed over the past 20 years. Your population would be the largest companies in the United States as determined by the S&P 500.

Step 2: Choose the Sample Size

Before picking the units within a population, we need to determine how many to select. This sample size may be constrained by the amount of time, capital rationing , or other resources available to analyze the sample. However, be mindful to pick a sample size large enough to be genuinely representative of the population. In the example above, there are constraints in analyzing the performance for every stock in the S&P 500, so we only want to analyze a subset of this population.

Example: Your sample size will be 20 companies from the S&P 500.

Step 3: Determine Population Units

In our example the items within the population are easy to determine, as they've already been identified for us (i.e., the companies listed within the S&P 500). However, imagine analyzing the students currently enrolled at a university or food products being sold at a grocery store. This step entails crafting the entire list of all items within your population.

Example: Using exchange information, you copy the companies comprising the S&P 500 into an Excel spreadsheet.

Step 4: Assign Numerical Values

The simple random sample process calls for every unit within the population to receive an unrelated numerical value. This is often assigned based on how the data may be filtered. For example, you could assign the numbers one to 500 to the companies based on market cap , alphabetical order, or company formation date. How the values are assigned isn’t relevant; all that matters is that each value is sequential and has an equal chance of being selected.

Example: You assign the numbers one through 500 to the companies in the S&P 500 based on alphabetical order of the current CEO's surname, with the first company receiving the value one and the last company receiving the value 500.

Step 5: Select Random Values

In step 2 we chose 20 as the number of items we wanted to analyze within our population. We now randomly select 20 number values out of the 500. There are multiple ways to do this, as discussed later in this article.

Example: Using a random number table (see below), you select the numbers 2, 7, 17, 67, 68, 75, 77, 87, 92, 101, 145, 201, 222, 232, 311, 333, 376, 401, 478, and 489.

Step 6: Identify the Sample

Each of the random variables selected in the prior step corresponds to an item within our population. The group sample is selected by identifying which random values were chosen and which population items those values match.

Example: Your sample consists of the companies that correspond to the values chosen in step 5.

There is no single method for determining the random values to be selected in step 5. The analyst can’t choose completely random numbers on their own, as there may be factors influencing their decision. For example, the analyst’s wedding anniversary may be the 24th, so they may consciously (or subconsciously) pick the random value 24. Instead, the analyst may choose one of the following methods:

• Random lottery : Each population number receives an equivalent item, say a ping pong ball or slip of paper, on which it is written, and those items are stored in a box. Random numbers are then selected by pulling items from the container without looking at them.
• Physical methods : Simple, early methods of random selection may use dice, flipping coins, or spinning wheels. Each outcome is assigned a value or outcome relating to the population.
• Random number table : Many statistics and research books contain sample tables with randomized numbers.
• Online random number generator : Many online tools exist where an analyst inputs first the population size and then the sample size to be selected.
• Random numbers from Excel : Numbers can be selected in Excel using the =RANDBETWEEN formula. A cell containing =RANDBETWEEN(1,5) will select a single random number between one and 5.

When pulling together a sample, consider getting assistance from a colleague or an independent person. They may be able to identify biases or discrepancies of which you may not be aware.

Simple Random vs. Other Sampling Methods

Simple random vs. stratified random sample.

A simple random sample is used to represent the entire data population. A stratified random sample divides the population into smaller groups, known as “strata,” based on shared characteristics.

Unlike simple random samples, stratified random samples are used with populations that can be easily broken into different subgroups or subsets. These groups are based on certain criteria, then elements from each are randomly chosen in proportion to the group’s size versus the population. In our example above, S&P 500 companies could have subsets defined by type of industry or geographical region of the company’s headquarters.

This method of sampling means there will be selections from each different group—the size of which is based on its proportion to the entire population. Researchers must ensure that the strata do not overlap. Every point in the population must only belong to one stratum, because they should be  mutually exclusive . Overlapping strata would increase the likelihood that some data are included, thus skewing the sample.

Simple Random vs. Systematic Sampling

Systematic sampling entails selecting a single random variable that determines the interval of how the population items are selected. For example, if the number 37 was chosen, the 37th company on the list sorted by last name of the CEO would be selected by the sample. Then, the 74th (i.e., the next 37th) and the 111st (i.e. the next 37th after that) would be added as well.

Simple random sampling does not have a starting point; therefore, there is the risk that the population items selected at random may cluster. In our example there may be an abundance of CEOs with a last name that starts with the letter 'F.' Systematic sampling strives to even further reduce bias by ensuring that these clusters do not happen.

Simple Random vs. Cluster Sampling

Cluster sampling (also known as “multistage random sampling”) can occur as a one-stage or two-stage cluster. In the former, items within a population are put into comparable groupings (using our example, companies are grouped by year formed), then sampling occurs within these clusters.

Two-stage cluster sampling occurs when clusters are formed through random selection. The population is not clustered with other similar items. Sample items are then randomly selected within each cluster.

Simple random sampling does not cluster any population sets. Clustering (especially two-stage clustering) can enhance the randomness of sample items. In addition, cluster sampling may provide a deeper analysis on a specific snapshot of a population, which may or may not enhance the analysis.

Advantages and Disadvantages of Simple Random Samples

While simple random samples are easy to use, they do come with key disadvantages that can render the data useless.

Advantages of a Simple Random Sample

Ease of use represents the biggest advantage of simple random sampling. Unlike more complicated sampling methods, such as stratified random sampling and probability sampling, there is no need to divide the population into subpopulations or take any other additional steps before selecting members of the population at random.

A simple random sample is meant to be an unbiased representation of a group. It is considered a fair way to select a sample from a larger population, as every member of the population has an equal chance of getting selected. Therefore, it has less chance of sampling bias.

Disadvantages of a Simple Random Sample

A sampling error can occur with a simple random sample if the sample does not end up accurately reflecting the population it is supposed to represent. For example, in a simple random sample of 25 employees, it would be possible to draw 25 men even if the population consisted of 125 women, 125 men, and 125 nonbinary people.

For this reason simple random sampling is more commonly used when the researcher knows little about the population. If the researcher knows more, it is better to use a different sampling technique, such as stratified random sampling, which helps to account for the differences within the population, such as age, race, or gender.

Other disadvantages include the fact that for sampling from large populations, the process can be time-consuming and costly compared with other methods. Researchers may find that a project not worth the endeavor of its cost-benefit analysis does not generate positive results.

As every unit has to be assigned an identifying or sequential number prior to the selection process, this task may be difficult based on the method of data collection or size of the data set.

Simple Random Sampling

Each item within a population has an equal chance of being selected.

There is less of a chance of sampling bias, as every item is randomly selected.

It is easy and convenient for data sets already listed or digitally stored.

Incomplete population demographics may exclude certain groups from being sampled.

Random selection means the sample may not be truly representative of the population.

Depending on the data set size and format, random sampling may be a time-intensive process.

Why Is a Simple Random Sample Simple?

No easier method exists to extract a research sample from a larger population than simple random sampling. Selecting enough subjects completely at random from the larger population also yields a sample that can be representative of the group being studied.

What Are Some Drawbacks of a Simple Random Sample?

Among the disadvantages of this technique are difficulty gaining access to respondents that can be drawn from the larger population, greater time, greater costs, and the fact that bias can still occur under certain circumstances.

What Is a Stratified Random Sample?

A stratified random sample first divides the population into smaller groups, or strata, based on shared characteristics. Therefore, a stratified sampling strategy will ensure that members from each subgroup are included in the data analysis. Stratified sampling is used to highlight differences among groups in a population, as opposed to simple random sampling, which treats all members of a population as equal, with an equal likelihood of being sampled.

How Are Random Samples Used?

Using simple random sampling allows researchers to make generalizations about a specific population and leave out any bias. Using statistical techniques, inferences and predictions can be made about the population without having to survey or collect data from every individual in that population.

Simple random sampling is the most basic form of analyzing a population, allowing every item within it to have the same probability of being selected. There are also more complicated sampling methods that attempt to correct for possible shortcomings in the simple method. However, they don’t match the ease of simple random sampling for smaller populations.

Business Research Methodology. " Simple Random Sampling ."

U.S. Department of Commerce: National Institute of Standards and Technology. " Appendix B. Random Number Tables. "

Microsoft. " RANDBETWEEN Function ."

Yale University. " Sampling. "

Penn State University Eberly College of Science. " 8.1 - Systematic Sampling ."

TGM Research. " An Ultimate Guide to Cluster Sampling: Types, Examples, and Applications ."

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Population health and population health metrics

• Jonathan M. Samet 1 &
• Shereen Hussein 2  

Population Health Metrics volume  22 , Article number:  19 ( 2024 ) Cite this article

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The title and purpose of our journal, Population Health Metrics, bring questions and comments—“Population Health Metrics aims to advance the science of population health assessment and welcomes papers relating to concepts, methods, ethics, applications, and summary measures of population health.” What is population health? How do we assess it? Why do we assess it? And more.

Those asked to define health inevitably cite the well-known definition from the 1948 Constitution for the World Health Organization (WHO): “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” [ 1 ] Population health , however, is a more recent term being used over the last couple of decades. In 2003, Kindig and Stoddart described the introduction of the term and the evolution of the concept of public health, leading to a definition: “we propose that population health as a concept of health be defined as “the health outcomes of a group of individuals, including the distribution of such outcomes within the group.” Other definitions have been offered (Table 1 ), and efforts have been made to distinguish population health and public health.

Population health can be conceptualized as the holistic assessment and enhancement of an entire community’s or population’s overall health outcomes and well-being, transcending the focus on individual sickness or specific risk factors that dominate health care. This approach emphasizes the collective health status of diverse demographic groups within a population, encompassing not only those currently experiencing illness but also individuals at varying levels of health, risks to health and vulnerability. Central to the concept of population health is recognizing the interconnected social, environmental, economic, and behavioral factors that influence health outcomes across populations. Thus, population health initiatives aim to address underlying determinants of health disparities and promote equitable access to resources and opportunities that support optimal health for all individuals, irrespective of their individual disease risks. Such initiatives are necessarily multidisciplinary.

Given the holistic nature of the health definition, population health assessment needs to be multidimensional and integrative. We have long had fundamental measures of population health, e.g., mortality rates, life expectancy, and disease incidence and prevalence. To capture morbidity, we now have Disability-Adjusted Life Years (DALYs) and Quality-Adjusted Life Years (QALYs). However, the need for population health assessment in the 21st century calls for more sensitive measures that capture heterogeneity and disparities within populations and provide insights for particularly susceptible and vulnerable subpopulations, e.g., the fetus and the elderly. Population health also needs to span the range of data systems globally, reaching from incomplete and manual vital registration systems to encompassing and automated national data systems. The former often involves dealing with non-standardized systems and missing data, while the latter may pose the analytical and computational challenges of “big data.” Challenges abound in contending with the diversity of data systems for assessing population health across the resource spectrum: for example, using verbal autopsies where healthcare access is limited at one extreme of resources and completing data linkages across multiple large databases at the other.

Measurement of disease burden has become intertwined with the concept of population health. Almost three decades ago, the methodology for estimating the disease burden was advanced by the WHO [ 2 ]. The concept of attribution of disease occurrence to risk factors was first advanced by Levin in a 1953 paper that described the calculation of population-attributable risk [ 3 ]. Citing the then-emerging literature on cigarette smoking and lung cancer, Levin proposed that if a risk factor caused a disease (his example being smoking and lung cancer), the proportion of disease caused by the factor is of interest. This principle underlies the massive undertaking of periodic disease burden estimation at the global, national, and subnational levels by the Institute for Health Metrics and Evaluation through its Global Burden of Disease project. The most recent estimates, just released, are for 2021 [ 4 ].

As to the “why” question, the answer is straightforward: We need a firm grasp of population health over time to identify where interventions are required and describe the consequences of existing interventions. Accurate population health monitoring is critical to decision-making and allocating the often scarce and inadequate resources available for dealing with problems and advancing population health.

The genesis of Population Health Metrics arose from the imperative to enhance the understanding of population health, facilitating the development of targeted interventions. Our mission is to disseminate research papers employing well-established methodologies or introducing innovative approaches for population health assessment, showcasing their practical applications. We strive to harness methodological advancements alongside the burgeoning availability of extensive datasets and machine learning techniques while remaining mindful of data scarcity in certain global regions. Moreover, we acknowledge the potential for novel methodologies to exploit limited information effectively. We are interested in how these new approaches to assessing population health will figure in decision-making. We particularly welcome papers that employ novel methods to utilize limited data in low- and middle-income countries (LMICs) for understanding population health and maximizing the utility of available information while addressing data scarcity challenges.

Our scope extends to papers exploring how population health measurements can inform decision-making processes and ways to optimize their utility. We advocate for a multidisciplinary approach, drawing insights from diverse fields such as public health, epidemiology, social sciences, data sciences, and policy. Through collaborative efforts, Population Health Metrics endeavors to promote comprehensive strategies and interventions that contribute to enduring enhancements in population-level health outcomes and advancing health equity.

With these emphases, some classes of papers are unlikely to fit well with Population Health Metrics. We often receive national and subnational surveys that generally offer results of national interest without bringing methodological advances. Such surveys and epidemiological studies of risk factors are not of interest, nor are clinical studies. We will consider systematic reviews on topics within the scope of the journal. We receive many papers that provide descriptive data analyses from the Global Burden of Disease (GBD) project. Most of these papers do not fit with the journal’s scope. We are, however, interested in papers that provide innovative uses of the GBD data, for example by linkages to other data resources to explore drivers of disease burden.

The concept of population health and the approaches to measuring it have evolved continually, taking the long-run view that evolution began centuries ago. We intend for Population Health Metrics to support that evolution, publishing papers that refine our assessment of population health.

World Health Organization. Constitution of the World Health Organization. 1948. http://apps.who.int/gb/bd/PDF/bd47/EN/constitution-en.pdf?ua=1 .

Murray CJ, Lopez AD, Organization WH. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020: summary. World Health Organization; 1996.

Levin ML. The occurrence of lung cancer in man. Acta Unio Int Contra Cancrum. 1953;9(3):531–41.

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The Lancet. Lancet Global Burden of Disease (GBD) Resource Center. Accessed May 29. 2024. https://www.thelancet.com/gbd .

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Acknowledgements

We would like to extend our appreciation to the Associate Editors of this journal, in particular Bruno Masquelier, José Penalvo, and Yafeng Wang, for their contributions which were instrumental in shaping the final manuscript.

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Jonathan M. Samet

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Samet, J.M., Hussein, S. Population health and population health metrics . Popul Health Metrics 22 , 19 (2024). https://doi.org/10.1186/s12963-024-00339-9

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Published : 02 August 2024

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