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3.3 Random Sampling and Data Collection

9 min read • december 29, 2022

Jed Quiaoit

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As alluded to in earlier sections, the big idea in this unit is that the way we collect data influences what we can and cannot say about a population.

To avoid the effects of bias on your data, the best method of implementing a study is by using random sampling in which a chance process is used to determine which members of a population are included in the sample. 📃

Types of Non-Biased Sampling Methods 

Simple random sample (srs).

A simple random sample (SRS) is a sample in which every group of a given size has an equal chance of being chosen. This means that every individual in the population has an equal chance of being selected for the sample, and that the sample is representative of the overall population.

There are several mechanisms that can be used to obtain a simple random sample, including numbering individuals and using a random number generator , using a table of random numbers , or drawing cards from a deck without replacement. These mechanisms ensure that the sample is selected randomly and that every group of a given size has an equal chance of being chosen. 🃏

Simple random sampling is a widely used sampling method because it is relatively easy to implement and provides a representative sample of the population. It is often used as a baseline comparison for other sampling methods, and is the basis for many types of sampling mechanisms. ✔️

In a calculator, SRS chooses a sample size “n” in a way that a group of individuals in the population has an equal chance to be selected as the sample. 

Choosing an SRS using a TI-84 Calculator:

Label each individual in the population with a different label from 1 to “N,” where N is the total number of individuals in the population. 

Randomize the way you choose the individuals for the sample. Use a random number generator to get “n” different integers from 1 to N, where n is the sample size. 

Select the individuals that were chosen by the calculator. 

NOTE: When an item from a population can be selected only once, this is called sampling without replacement . When an item from the population can be selected more than once, this is called sampling with replacement .

Stratified Random Sample

Strata are groups of individuals in a population who share characteristics thought to be associated with the variables being measured in a study. 📚

A stratified random sample involves dividing the population into separate strata, based on shared characteristics or attributes. This ensures that the sample is representative of the overall population in terms of these characteristics.

Within each stratum, a simple random sample is then selected using one of the mechanisms described above, such as numbering individuals and using a random number generator or using a table of random numbers . The selected units from each stratum are then combined to form the final sample.

Stratified random sampling is often used when the population is heterogeneous , or diverse, in terms of the characteristics being studied. By dividing the population into strata based on these characteristics, researchers can ensure that the sample is representative of the overall population in terms of these characteristics.

Stratified random samples also reduce variability in the data and give more precise results.

Source: Blogspot

Remember the practice question in the previous section about confounding variables? We could use those as strata!

An example of stratified random sampling might involve conducting a study to investigate the relationship between diet and heart disease. To ensure that the sample is representative of the overall population, the population might be stratified based on age , gender , and income .

We can, then, number individuals and using a random number generator or using a table of random numbers . The selected units from each stratum would then be combined to form the final sample.

This approach would ensure that the sample is representative of the overall population in terms of age, gender, and income, allowing researchers to more accurately interpret the results of the study. It would also allow researchers to investigate any potential interactions between these variables and the relationship between diet and heart disease.

Cluster Sample

A cluster sample involves the division of a population into smaller groups, called clusters . Ideally, there is heterogeneity within each cluster, and clusters are similar to one another in their composition. A simple random sample of clusters is selected from the population to form the sample of clusters. Data are collected from all observations in the selected clusters.

How is this different from stratified sampling? In a cluster sampling design, the population is first divided into smaller groups, or clusters, and a sample of these clusters is selected. Data is then collected from all observations within the selected clusters.

One of the main advantages of cluster sampling is that it can be more cost-effective and efficient than simple random sampling , especially when the population is spread out over a large geographic area. It is also useful when it is difficult to obtain a complete list of the individuals in the population, as is often the case in developing countries or in studying hard-to-reach populations.

However, cluster sampling can also introduce bias if the clusters are not representative of the overall population. It is important to carefully consider the sampling frame and the sampling method to ensure that the sample is representative of the population.

Source: Wikipedia

Imagine that you're a researcher who wants to study the attitudes of high school students towards school lunches. You want to study a sample of high schools in a large city to get a sense of the attitudes of students across the city. Instead of sampling students from all the high schools in the city individually (which would be time-consuming and expensive), you decide to use cluster sampling.

First, you divide the high schools in the city into clusters based on geographic location (e.g., north, south, east, west). Then, you randomly select a sample of these clusters (e.g., you might randomly select the north and west clusters). Finally, you collect data from all the students in the selected clusters to get a sample of students from across the city.

In this example, the population is all high school students in the city, the clusters are the geographic regions of the high schools, and the sample is the students in the geographic region selected.

Systematic Random Sample

A systematic random sample is unique in which sample members are selected from a population by starting at a randomly chosen point and then selecting every kth element from the sampling frame, where k is the periodic interval .

For example, if you have a list of 1000 people and you want to select a sample of 100 people using a systematic random sample, you might choose a random starting point between 1 and 10, and then select every 10th person on the list (e.g., the 1st person, the 11th person, the 21st person, and so on). This ensures that every member of the population has an equal probability of being selected.

Systematic random sampling is a popular sampling method because it is relatively easy to implement and it can be more efficient than simple random sampling . However, it is important to ensure that the periodic interval (k) is chosen correctly to avoid introducing bias into the sample. For example, if the periodic interval is not chosen randomly, it may result in oversampling or undersampling of certain subgroups within the population.

Imagine that you are a researcher who wants to study the attitudes of grocery store customers towards the store's loyalty program. You want to study a sample of customers from a large grocery store chain to get a sense of the attitudes of customers across the chain. Instead of sampling customers from all the stores in the chain individually (which would be time-consuming and expensive), you decide to use systematic random sampling .

First, you create a list of all the customers who have shopped at the store in the past month. This list will be your sampling frame. Next, you choose a random starting point on the list (e.g., you might choose the 15th person on the list). Finally, you select every 10th person on the list after the starting point (e.g., the 15th person, the 25th person, the 35th person, and so on). This will give you a sample of customers from across the chain.

In this example, the population is all customers who have shopped at the store in the past month, the sampling frame is the list of customers, and the sample is the customers selected according to the periodic interval .

Altogether, being able to identify which method to use depends on what the question is asking. Identify the population and variables to figure out how large of a difference there is between the sample. Make your decision based on that information.

Source: Scribbr

Practice Problem

You are a researcher who wants to study the attitudes of college students towards climate change. Your goal is to get a sense of the attitudes of college students across the United States. You have a budget of $10,000 and six months to complete the study.

There are three potential sampling methods that you could use:

Simple random sampling : You could create a list of all the college students in the United States and use a random number generator to select a sample of students from the list. This method would ensure that every student in the population has an equal chance of being selected, but it would be time-consuming and expensive to create a complete list of all the college students in the United States.

Cluster sampling: You could divide the college students in the United States into clusters based on geographic location (e.g., east coast, west coast, midwest) and randomly select a sample of these clusters. You could then collect data from all the students in the selected clusters. This method would be more efficient and cost-effective than simple random sampling , but it could introduce bias if the clusters are not representative of the overall population.

Systematic random sampling : You could create a list of all the college students in the United States and choose a random starting point on the list. You could then select every 100th student on the list after the starting point to get a sample of students from across the United States. This method would be relatively easy to implement and could be more efficient than simple random sampling , but it could introduce bias if the periodic interval (100 students) is not chosen randomly.

Which sampling method do you think would be the best to use in this situation, and why?

In this situation, cluster sampling might be the best method to use because it would be more efficient and cost-effective than simple random sampling , and it would not require creating a complete list of all the college students in the United States. However, it is important to ensure that the clusters are representative of the overall population to avoid introducing bias into the sample.

Here's the fun part: AP Stats graders are actually open-minded if you picked either simple random sampling or systematic random sampling (instead of cluster sampling). Your job is to create a very persuasive argument that'll convince them that either sampling method might work, too (in comparison to the other two)!

🎥 Watch: AP Stats - Sampling Methods and Sources of Bias

Key Terms to Review ( 9 )

Periodic Interval

Random Number Generator

Random Sampling

Sampling with replacement

Sampling without replacement

Simple random sample (SRS)

Table of Random Numbers

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Random Assignment in Psychology: Definition & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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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.

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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In psychology, random assignment refers to the practice of allocating participants to different experimental groups in a study in a completely unbiased way, ensuring each participant has an equal chance of being assigned to any group.

In experimental research, random assignment, or random placement, organizes participants from your sample into different groups using randomization. 

Random assignment uses chance procedures to ensure that each participant has an equal opportunity of being assigned to either a control or experimental group.

The control group does not receive the treatment in question, whereas the experimental group does receive the treatment.

When using random assignment, neither the researcher nor the participant can choose the group to which the participant is assigned. This ensures that any differences between and within the groups are not systematic at the onset of the study. 

In a study to test the success of a weight-loss program, investigators randomly assigned a pool of participants to one of two groups.

Group A participants participated in the weight-loss program for 10 weeks and took a class where they learned about the benefits of healthy eating and exercise.

Group B participants read a 200-page book that explains the benefits of weight loss. The investigator randomly assigned participants to one of the two groups.

The researchers found that those who participated in the program and took the class were more likely to lose weight than those in the other group that received only the book.

Importance 

Random assignment ensures that each group in the experiment is identical before applying the independent variable.

In experiments , researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. Random assignment increases the likelihood that the treatment groups are the same at the onset of a study.

Thus, any changes that result from the independent variable can be assumed to be a result of the treatment of interest. This is particularly important for eliminating sources of bias and strengthening the internal validity of an experiment.

Random assignment is the best method for inferring a causal relationship between a treatment and an outcome.

Random Selection vs. Random Assignment 

Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study.

On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. 

Random selection ensures that everyone in the population has an equal chance of being selected for the study. Once the pool of participants has been chosen, experimenters use random assignment to assign participants into groups. 

Random assignment is only used in between-subjects experimental designs, while random selection can be used in a variety of study designs.

Random Assignment vs Random Sampling

Random sampling refers to selecting participants from a population so that each individual has an equal chance of being chosen. This method enhances the representativeness of the sample.

Random assignment, on the other hand, is used in experimental designs once participants are selected. It involves allocating these participants to different experimental groups or conditions randomly.

This helps ensure that any differences in results across groups are due to manipulating the independent variable, not preexisting differences among participants.

When to Use Random Assignment

Random assignment is used in experiments with a between-groups or independent measures design.

In these research designs, researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables.

There is usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable at the onset of the study.

How to Use Random Assignment

There are a variety of ways to assign participants into study groups randomly. Here are a handful of popular methods: 

• Random Number Generator : Give each member of the sample a unique number; use a computer program to randomly generate a number from the list for each group.
• Lottery : Give each member of the sample a unique number. Place all numbers in a hat or bucket and draw numbers at random for each group.
• Flipping a Coin : Flip a coin for each participant to decide if they will be in the control group or experimental group (this method can only be used when you have just two groups) 
• Roll a Die : For each number on the list, roll a dice to decide which of the groups they will be in. For example, assume that rolling 1, 2, or 3 places them in a control group and rolling 3, 4, 5 lands them in an experimental group.

When is Random Assignment not used?

• When it is not ethically permissible: Randomization is only ethical if the researcher has no evidence that one treatment is superior to the other or that one treatment might have harmful side effects. 
• When answering non-causal questions : If the researcher is just interested in predicting the probability of an event, the causal relationship between the variables is not important and observational designs would be more suitable than random assignment. 
• When studying the effect of variables that cannot be manipulated: Some risk factors cannot be manipulated and so it would not make any sense to study them in a randomized trial. For example, we cannot randomly assign participants into categories based on age, gender, or genetic factors.

Drawbacks of Random Assignment

While randomization assures an unbiased assignment of participants to groups, it does not guarantee the equality of these groups. There could still be extraneous variables that differ between groups or group differences that arise from chance. Additionally, there is still an element of luck with random assignments.

Thus, researchers can not produce perfectly equal groups for each specific study. Differences between the treatment group and control group might still exist, and the results of a randomized trial may sometimes be wrong, but this is absolutely okay.

Scientific evidence is a long and continuous process, and the groups will tend to be equal in the long run when data is aggregated in a meta-analysis.

Additionally, external validity (i.e., the extent to which the researcher can use the results of the study to generalize to the larger population) is compromised with random assignment.

Random assignment is challenging to implement outside of controlled laboratory conditions and might not represent what would happen in the real world at the population level. 

Random assignment can also be more costly than simple observational studies, where an investigator is just observing events without intervening with the population.

Randomization also can be time-consuming and challenging, especially when participants refuse to receive the assigned treatment or do not adhere to recommendations. 

What is the difference between random sampling and random assignment?

Random sampling refers to randomly selecting a sample of participants from a population. Random assignment refers to randomly assigning participants to treatment groups from the selected sample.

Does random assignment increase internal validity?

Yes, random assignment ensures that there are no systematic differences between the participants in each group, enhancing the study’s internal validity .

Does random assignment reduce sampling error?

Yes, with random assignment, participants have an equal chance of being assigned to either a control group or an experimental group, resulting in a sample that is, in theory, representative of the population.

Random assignment does not completely eliminate sampling error because a sample only approximates the population from which it is drawn. However, random sampling is a way to minimize sampling errors. 

When is random assignment not possible?

Random assignment is not possible when the experimenters cannot control the treatment or independent variable.

For example, if you want to compare how men and women perform on a test, you cannot randomly assign subjects to these groups.

Participants are not randomly assigned to different groups in this study, but instead assigned based on their characteristics.

Does random assignment eliminate confounding variables?

Yes, random assignment eliminates the influence of any confounding variables on the treatment because it distributes them at random among the study groups. Randomization invalidates any relationship between a confounding variable and the treatment.

Why is random assignment of participants to treatment conditions in an experiment used?

Random assignment is used to ensure that all groups are comparable at the start of a study. This allows researchers to conclude that the outcomes of the study can be attributed to the intervention at hand and to rule out alternative explanations for study results.

Further Reading

• Bogomolnaia, A., & Moulin, H. (2001). A new solution to the random assignment problem .  Journal of Economic theory ,  100 (2), 295-328.
• Krause, M. S., & Howard, K. I. (2003). What random assignment does and does not do .  Journal of Clinical Psychology ,  59 (7), 751-766.

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AP Statistics : How to do random assignments in an experiment

Study concepts, example questions & explanations for ap statistics, all ap statistics resources, example questions, example question #1 : how to conduct an experiment.

A researcher wants to randomly assign participants to a treatment and control group. Which of the following approaches ensures that the treatment assignment is random?

Obtaining nationally representative samples for both

Flipping a coin

Assigning the treatment based on who needs it the most 

Assigning the treatment by gender

The only random procedure here is the coin flip. In expectation, the coin flip ensures that no background variables influence treatment assignment whereas the other examples either have nothing to do with random assignment (e.g. nationally representative sample) or completely contradict the purpose of random assignment (e.g. assigning the treatment based on who needs it the most). 

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Statistics Made Easy

Random Selection vs. Random Assignment

Random selection and random assignment  are two techniques in statistics that are commonly used, but are commonly confused.

Random selection  refers to the process of randomly selecting individuals from a population to be involved in a study.

Random assignment  refers to the process of randomly  assigning  the individuals in a study to either a treatment group or a control group.

You can think of random selection as the process you use to “get” the individuals in a study and you can think of random assignment as what you “do” with those individuals once they’re selected to be part of the study.

The Importance of Random Selection and Random Assignment

When a study uses  random selection , it selects individuals from a population using some random process. For example, if some population has 1,000 individuals then we might use a computer to randomly select 100 of those individuals from a database. This means that each individual is equally likely to be selected to be part of the study, which increases the chances that we will obtain a representative sample – a sample that has similar characteristics to the overall population.

By using a representative sample in our study, we’re able to generalize the findings of our study to the population. In statistical terms, this is referred to as having  external validity – it’s valid to externalize our findings to the overall population.

When a study uses  random assignment , it randomly assigns individuals to either a treatment group or a control group. For example, if we have 100 individuals in a study then we might use a random number generator to randomly assign 50 individuals to a control group and 50 individuals to a treatment group.

By using random assignment, we increase the chances that the two groups will have roughly similar characteristics, which means that any difference we observe between the two groups can be attributed to the treatment. This means the study has  internal validity  – it’s valid to attribute any differences between the groups to the treatment itself as opposed to differences between the individuals in the groups.

Examples of Random Selection and Random Assignment

It’s possible for a study to use both random selection and random assignment, or just one of these techniques, or neither technique. A strong study is one that uses both techniques.

The following examples show how a study could use both, one, or neither of these techniques, along with the effects of doing so.

Example 1: Using both Random Selection and Random Assignment

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 individuals to be in the study by using a computer to randomly select 100 names from a database. Once they have the 100 individuals, they once again use a computer to randomly assign 50 of the individuals to a control group (e.g. stick with their standard diet) and 50 individuals to a treatment group (e.g. follow the new diet). They record the total weight loss of each individual after one month.

Results:  The researchers used random selection to obtain their sample and random assignment when putting individuals in either a treatment or control group. By doing so, they’re able to generalize the findings from the study to the overall population  and  they’re able to attribute any differences in average weight loss between the two groups to the new diet.

Example 2: Using only Random Selection

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 individuals to be in the study by using a computer to randomly select 100 names from a database. However, they decide to assign individuals to groups based solely on gender. Females are assigned to the control group and males are assigned to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers used random selection to obtain their sample, but they did not use random assignment when putting individuals in either a treatment or control group. Instead, they used a specific factor – gender – to decide which group to assign individuals to. By doing this, they’re able to generalize the findings from the study to the overall population but they are  not  able to attribute any differences in average weight loss between the two groups to the new diet. The internal validity of the study has been compromised because the difference in weight loss could actually just be due to gender, rather than the new diet.

Example 3: Using only Random Assignment

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 males athletes to be in the study. Then, they use a computer program to randomly assign 50 of the male athletes to a control group and 50 to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers did not use random selection to obtain their sample since they specifically chose 100 male athletes. Because of this, their sample is not representative of the overall population so their external validity is compromised – they will not be able to generalize the findings from the study to the overall population. However, they did use random assignment, which means they can attribute any difference in weight loss to the new diet.

Example 4: Using Neither Technique

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 50 males athletes and 50 female athletes to be in the study. Then, they assign all of the female athletes to the control group and all of the male athletes to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers did not use random selection to obtain their sample since they specifically chose 100 athletes. Because of this, their sample is not representative of the overall population so their external validity is compromised – they will not be able to generalize the findings from the study to the overall population. Also, they split individuals into groups based on gender rather than using random assignment, which means their internal validity is also compromised – differences in weight loss might be due to gender rather than the diet.

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Randomized Block Design ( Topics 3.5-3.6 )

Chapter 4 - day 8, day 12 day 13, learning targets.

Describe a completely randomized design for an experiment.

Describe a randomized block design and a matched pairs design for an experiment and explain the purpose of blocking in an experiment.

Activity: Does the Type of SAT Prep Matter?

Answer Key:

This activity walks students through three different experimental designs, in increasing complexity.

Completely randomized design

Block design

Matched pairs design

Notice that the matched pairs design is really just a fancy version of a block design, where each block is of size 2 (two students paired with similar GPAs).

Which variable should be used for blocking? Why block?

Should we create blocks of Juniors and Seniors?  Blocks of males and females?  Blocks of high GPA and low GPA?

Which variable?   Choose the one that is most strongly associated with the response variable.  We think grade level is the variable that is most strongly associated with differences in SAT scores.

Why block?  To reduce the variability in the response variable (SAT score).  The variability of SAT scores should be less in each block then it would be for the whole group.  This can help make it easier to determine if results are statistically significant (increase power).  To dig deeper into this idea, consider doing the  Dog Blocking Activity .

The thinking and reasoning here is very similar to making the choice of variable to use when doing a stratified random sample (like the choice of stratifying by row in the  Justin Timberlake lesson ). It is easy to look back at this lesson and make the connection (hindsight is  20/20 ).

Luke's Lesson Notes

Here is a brief video highlighting some key information to help you prepare to teach this lesson.

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The Definition of Random Assignment According to Psychology

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

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Materio / Getty Images

Random assignment refers to the use of chance procedures in psychology experiments to ensure that each participant has the same opportunity to be assigned to any given group in a study to eliminate any potential bias in the experiment at the outset. Participants are randomly assigned to different groups, such as the treatment group versus the control group. In clinical research, randomized clinical trials are known as the gold standard for meaningful results.

Simple random assignment techniques might involve tactics such as flipping a coin, drawing names out of a hat, rolling dice, or assigning random numbers to a list of participants. It is important to note that random assignment differs from random selection .

While random selection refers to how participants are randomly chosen from a target population as representatives of that population, random assignment refers to how those chosen participants are then assigned to experimental groups.

Random Assignment In Research

To determine if changes in one variable will cause changes in another variable, psychologists must perform an experiment. Random assignment is a critical part of the experimental design that helps ensure the reliability of the study outcomes.

Researchers often begin by forming a testable hypothesis predicting that one variable of interest will have some predictable impact on another variable.

The variable that the experimenters will manipulate in the experiment is known as the independent variable , while the variable that they will then measure for different outcomes is known as the dependent variable. While there are different ways to look at relationships between variables, an experiment is the best way to get a clear idea if there is a cause-and-effect relationship between two or more variables.

Once researchers have formulated a hypothesis, conducted background research, and chosen an experimental design, it is time to find participants for their experiment. How exactly do researchers decide who will be part of an experiment? As mentioned previously, this is often accomplished through something known as random selection.

Random Selection

In order to generalize the results of an experiment to a larger group, it is important to choose a sample that is representative of the qualities found in that population. For example, if the total population is 60% female and 40% male, then the sample should reflect those same percentages.

Choosing a representative sample is often accomplished by randomly picking people from the population to be participants in a study. Random selection means that everyone in the group stands an equal chance of being chosen to minimize any bias. Once a pool of participants has been selected, it is time to assign them to groups.

By randomly assigning the participants into groups, the experimenters can be fairly sure that each group will have the same characteristics before the independent variable is applied.

Participants might be randomly assigned to the control group , which does not receive the treatment in question. The control group may receive a placebo or receive the standard treatment. Participants may also be randomly assigned to the experimental group , which receives the treatment of interest. In larger studies, there can be multiple treatment groups for comparison.

There are simple methods of random assignment, like rolling the die. However, there are more complex techniques that involve random number generators to remove any human error.

There can also be random assignment to groups with pre-established rules or parameters. For example, if you want to have an equal number of men and women in each of your study groups, you might separate your sample into two groups (by sex) before randomly assigning each of those groups into the treatment group and control group.

Random assignment is essential because it increases the likelihood that the groups are the same at the outset. With all characteristics being equal between groups, other than the application of the independent variable, any differences found between group outcomes can be more confidently attributed to the effect of the intervention.

Example of Random Assignment

Imagine that a researcher is interested in learning whether or not drinking caffeinated beverages prior to an exam will improve test performance. After randomly selecting a pool of participants, each person is randomly assigned to either the control group or the experimental group.

The participants in the control group consume a placebo drink prior to the exam that does not contain any caffeine. Those in the experimental group, on the other hand, consume a caffeinated beverage before taking the test.

Participants in both groups then take the test, and the researcher compares the results to determine if the caffeinated beverage had any impact on test performance.

A Word From Verywell

Random assignment plays an important role in the psychology research process. Not only does this process help eliminate possible sources of bias, but it also makes it easier to generalize the results of a tested sample of participants to a larger population.

Random assignment helps ensure that members of each group in the experiment are the same, which means that the groups are also likely more representative of what is present in the larger population of interest. Through the use of this technique, psychology researchers are able to study complex phenomena and contribute to our understanding of the human mind and behavior.

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Sullivan L. Random assignment versus random selection . In: The SAGE Glossary of the Social and Behavioral Sciences. SAGE Publications, Inc.; 2009. doi:10.4135/9781412972024.n2108

Alferes VR. Methods of Randomization in Experimental Design . SAGE Publications, Inc.; 2012. doi:10.4135/9781452270012

Nestor PG, Schutt RK. Research Methods in Psychology: Investigating Human Behavior. (2nd Ed.). SAGE Publications, Inc.; 2015.

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