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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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- Random Assignment in Experiments | Introduction & Examples
Random Assignment in Experiments | Introduction & Examples
Published on March 8, 2021 by Pritha Bhandari . Revised on February 13, 2023.
In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomization.
With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomized designs .
Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors, not research biases like sampling bias or selection bias .
Table of contents
Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, frequently asked questions about random assignment.
Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment and avoid biases.
In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.
This is called a between-groups or independent measures design.
You use three groups of participants that are each given a different level of the independent variable:
- a control group that’s given a placebo (no dosage, to control for a placebo effect ),
- an experimental group that’s given a low dosage,
- a second experimental group that’s given a high dosage.
Random assignment to helps you make sure that the treatment groups don’t differ in systematic ways at the start of the experiment, as this can seriously affect (and even invalidate) your work.
If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.
- participants recruited from cafes are placed in the control group ,
- participants recruited from local community centers are placed in the low dosage experimental group,
- participants recruited from gyms are placed in the high dosage group.
With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym-users may tend to engage in more healthy behaviors than people who frequent cafes or community centers, and this would introduce a healthy user bias in your study.
Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.
Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.
Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.
Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups.
While random sampling is used in many types of studies, random assignment is only used in between-subjects experimental designs.
Some studies use both random sampling and random assignment, while others use only one or the other.
Random sampling enhances the external validity or generalizability of your results, because it helps ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .
You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.
Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .
- a control group that receives no intervention.
- an experimental group that has a remote team-building intervention every week for a month.
You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.
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To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.
- Random number generator: Use a computer program to generate random numbers from the list for each group.
- Lottery method: Place all numbers individually in a hat or a bucket, and draw numbers at random for each group.
- Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
- Use a dice: When you have three groups, 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 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.
This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.
Random assignment in block designs
In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .
For example, a randomized block design involves placing participants into blocks based on a shared characteristic (e.g., college students versus graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.
In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.
Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.
When comparing different groups
Sometimes, differences between participants are the main focus of a study, for example, when comparing men and women or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.
In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women, etc.). All participants are tested the same way, and then their group-level outcomes are compared.
When it’s not ethically permissible
When studying unhealthy or dangerous behaviors, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.
When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers). These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.
In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.
Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.
In contrast, random assignment is a way of sorting the sample into control and experimental groups.
Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.
Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.
In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.
To implement random assignment , assign a unique number to every member of your study’s sample .
Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.
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Unit 6: lesson 5.
- Statistical significance of experiment
Random sampling vs. random assignment (scope of inference)
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Difference between Random Selection and Random Assignment
Random selection and random assignment are commonly confused or used interchangeably, though the terms refer to entirely different processes. Random selection refers to how sample members (study participants) are selected from the population for inclusion in the study. Random assignment is an aspect of experimental design in which study participants are assigned to the treatment or control group using a random procedure.
Random selection requires the use of some form of random sampling (such as stratified random sampling , in which the population is sorted into groups from which sample members are chosen randomly). Random sampling is a probability sampling method, meaning that it relies on the laws of probability to select a sample that can be used to make inference to the population; this is the basis of statistical tests of significance .
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Random assignment takes place following the selection of participants for the study. In a true experiment, all study participants are randomly assigned either to receive the treatment (also known as the stimulus or intervention) or to act as a control in the study (meaning they do not receive the treatment). Although random assignment is a simple procedure (it can be accomplished by the flip of a coin), it can be challenging to implement outside of controlled laboratory conditions.
A study can use both, only one, or neither. Here are some examples to illustrate each situation:
A researcher gets a list of all students enrolled at a particular school (the population). Using a random number generator, the researcher selects 100 students from the school to participate in the study (the random sample). All students’ names are placed in a hat and 50 are chosen to receive the intervention (the treatment group), while the remaining 50 students serve as the control group. This design uses both random selection and random assignment.
A study using only random assignment could ask the principle of the school to select the students she believes are most likely to enjoy participating in the study, and the researcher could then randomly assign this sample of students to the treatment and control groups. In such a design the researcher could draw conclusions about the effect of the intervention but couldn’t make any inference about whether the effect would likely to be found in the population.
A study using only random selection could randomly select students from the overall population of the school, but then assign students in one grade to the intervention and students in another grade to the control group. While any data collected from this sample could be used to make inference to the population of the school, the lack of random assignment to be in the treatment or control group would make it impossible to conclude whether the intervention had any effect.
Random selection is thus essential to external validity, or the extent to which the researcher can use the results of the study to generalize to the larger population. Random assignment is central to internal validity, which allows the researcher to make causal claims about the effect of the treatment. Nonrandom assignment often leads to non-equivalent groups, meaning that any effect of the treatment might be a result of the groups being different at the outset rather than different at the end as a result of the treatment. The consequences of random selection and random assignment are clearly very different, and a strong research design will employ both whenever possible to ensure both internal and external validity .
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The Definition of Random Assignment According to Psychology
Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.
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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. Study participants are randomly assigned to different groups, such as the experimental group or treatment group.
Random assignment might involve tactics such as flipping a coin, drawing names out of a hat, rolling dice, or assigning random numbers to participants.
It is important to note that random assignment differs from random selection . While random selection refers to how participants are randomly chosen to represent the larger 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 lead to changes in another variable, psychologists must perform an experiment. Researchers often begin by forming a testable hypothesis predicting that one variable of interest will have some 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 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.
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 51% female and 49% 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. Once a pool of participants has been selected, it is time to assign them into groups.
By randomly assigning the participants into groups, the experimenters can be fairly sure that each group will be the same before the independent variable is applied.
Participants might be randomly assigned to the control group , which does not receive the treatment in question. Or they might be randomly assigned to the experimental group , which does receive the treatment.
Random assignment increases the likelihood that the two groups are the same at the outset. That way any changes that result from the application of the independent variable can be assumed to be the result of the treatment of interest.
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 population 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. 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.
Sullivan L. Random assignment versus random selection . In: The SAGE Glossary of the Social and Behavioral Sciences. Thousand Oaks: SAGE Publications, Inc.; 2009. doi:10.4135/9781412972024.n2108
Lin Y, Zhu M, Su Z. The pursuit of balance: An overview of covariate-adaptive randomization techniques in clinical trials . Contemp Clin Trials. 2015;45(Pt A):21-25. doi:10.1016/j.cct.2015.07.011
Alferes VR. Methods of Randomization in Experimental Design. Los Angeles: SAGE; 2012.
Nestor PG, Schutt RK. Research Methods in Psychology: Investigating Human Behavior. Los Angeles: SAGE; 2015.
By Kendra Cherry Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.
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What Is Random Selection?
Random selection refers to a process that researchers use to pick participants for a study. When using this method, every single member of a population has an equal chance of being chosen as a subject. This process is an important research tool used in psychology research, allowing scientists to create representative samples from which conclusions can be drawn and applied to the larger population.
Random Selection vs. Random Assignment: What’s the Difference?
One thing that is important to note is that random selection is not the same thing as random assignment . While random selection involves how participants are chosen for a study, random assignment involves how those chosen are then assigned to different groups in the experiment. Many studies and experiments actually use both of these techniques.
Why Do Researchers Use Random Selection?
Some key reasons include:
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- Types of Designs
- Probabilistic Equivalence
Random Selection & Assignment
- Defining Experimental Designs
- Factorial Designs
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- Covariance Designs
- Hybrid Experimental Designs
- Quasi-Experimental Design
- Pre-Post Design Relationships
- Designing Designs for Research
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Random selection is how you draw the sample of people for your study from a population. Random assignment is how you assign the sample that you draw to different groups or treatments in your study.
It is possible to have both random selection and assignment in a study. Let’s say you drew a random sample of 100 clients from a population list of 1000 current clients of your organization. That is random sampling. Now, let’s say you randomly assign 50 of these clients to get some new additional treatment and the other 50 to be controls. That’s random assignment.
It is also possible to have only one of these (random selection or random assignment) but not the other in a study. For instance, if you do not randomly draw the 100 cases from your list of 1000 but instead just take the first 100 on the list, you do not have random selection. But you could still randomly assign this nonrandom sample to treatment versus control. Or, you could randomly select 100 from your list of 1000 and then nonrandomly (haphazardly) assign them to treatment or control.
And, it’s possible to have neither random selection nor random assignment. In a typical nonequivalent groups design in education you might nonrandomly choose two 5th grade classes to be in your study. This is nonrandom selection. Then, you could arbitrarily assign one to get the new educational program and the other to be the control. This is nonrandom (or nonequivalent) assignment.
Random selection is related to sampling . Therefore it is most related to the external validity (or generalizability) of your results. After all, we would randomly sample so that our research participants better represent the larger group from which they’re drawn. Random assignment is most related to design . In fact, when we randomly assign participants to treatments we have, by definition, an experimental design . Therefore, random assignment is most related to internal validity . After all, we randomly assign in order to help assure that our treatment groups are similar to each other (i.e., equivalent) prior to the treatment.
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Random assignment vs random sampling
Terms in this set (2)
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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.
What's the difference between random assignment and random selection? Random selection, or random sampling, is a way of selecting members of a population for your study's sample. In contrast, random assignment is a way of sorting the sample into control and experimental groups.
Random sampling and random assignment are both important concepts in research, but it's important to understand the difference between them. Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study.
Random sampling refers to the method you use to select individuals from the population to participate in your study. In other words, random sampling means that you are randomly selecting individuals from the population to participate in your study.
Random sampling vs. random assignment (scope of inference) AP.STATS: VAR‑3 (EU) , VAR‑3.E (LO) , VAR‑3.E.1 (EK) , VAR‑3.E.2 (EK) , VAR‑3.E.3 (EK) , VAR‑3.E.4 (EK) CCSS.Math: HSS.IC.B.3 , HSS.IC.B Google Classroom Hilary wants to determine if any relationship exists between Vitamin D and blood pressure.
Random selection means to create your study sample randomly, by chance. Random selection results in a representative sample; you can make generalizations and predictions about a population's behavior based on your sample as long as you have used a probability sampling method. The word "random" has a precise meaning in statistics.
Random selection refers to how sample members (study participants) are selected from the population for inclusion in the study. Random assignment is an aspect of experimental design in which study participants are assigned to the treatment or control group using a random procedure.
Random selection refers to how the sample is drawn from the population as a whole, while random assignment refers to how the participants are then assigned to either the experimental or control groups. It is possible to have both random selection and random assignment in an experiment.
Random selection. Selection of participants using a random sampling method. Random assignment. Placement of participants into experimental conditions on the basis of a chance process. Purpose of random assignment. To produce two or more equivalent groups for use in an experiment. Purpose of random selection. To obtain a representative sample.
It is important to note that random assignment differs from random selection. While random selection refers to how participants are randomly chosen to represent the larger population, random assignment refers to how those chosen participants are then assigned to experimental groups. 1 Random Assignment In Research
Random Assignment vs Random Selection - STAT 8050 - Intermed Math Stat - StuDocu Sign in Register Sign in Register Home My Library Courses You don't have any courses yet. Books You don't have any books yet. Studylists You don't have any Studylists yet. Recent Documents You haven't viewed any documents yet. Discovery Institutions
studying. For example, in the serif/sans serif example, random assignment helps us create treatment groups that are similar to each other, and the only difference between them is that one group reads text in serif font and the other in sans serif font. Therefore, causality can be inferred. Statistics 101 (Duke University) Random sampling vs ...
Random Selection vs. Random Assignment: What's the Difference? One thing that is important to note is that random selection is not the same thing as random assignment. While random selection involves how participants are chosen for a study, random assignment involves how those chosen are then assigned to different groups in the experiment ...
Random Selection & Assignment Random selection is how you draw the sample of people for your study from a population. Random assignment is how you assign the sample that you draw to different groups or treatments in your study. It is possible to have both random selection and assignment in a study.
This video discusses random sampling and random assignment, and concepts of generalizability and causality.
No. Random selection, also called random sampling, is the process of choosing all the participants in a study. After the participants are chosen, random allocation, also called random...
Random Sampling vs. Random Assignment 1,931 views Jun 7, 2019 16 Dislike Share Jason Popan 1.03K subscribers A brief explanation of the distinction between random sampling and random...
Ch. 14: Random Sampling, Random Assignment, and Causality. Term. 1 / 8. Random Sampling. Click the card to flip 👆. Definition. 1 / 8. when each observation in a population has an equal chance of occurring in the sample; list every observation in the population then use an unbiased method of choosing n observations from the list.
Random sampling is a process for obtaining a sample that accurately represents a population. Random assignment uses a chance process to assign subjects to experimental groups. Using random assignment requires that the experimenters can control the group assignment for all study subjects.
Terms in this set (2) Random Sampling. - research done before the participants are selected. It is used to recruit participants into the study. Random assignment. - the participants are already in the study and are randomly assigned to an intervention or control group.