what is control and experimental group mean

Control Group vs. Experimental Group

What's the difference.

Control group and experimental group are two essential components of a scientific experiment. The control group serves as a baseline for comparison, as it does not receive any treatment or intervention. It helps researchers determine the natural or expected outcome of the experiment. On the other hand, the experimental group is exposed to the independent variable or the treatment being tested. By comparing the results of the control group with the experimental group, researchers can assess the effectiveness or impact of the treatment. The control group provides a reference point, while the experimental group allows for the evaluation of the specific variable being studied.

Further Detail

Introduction.

In scientific research, control groups and experimental groups play crucial roles in understanding the effects of variables and determining causality. These groups are essential in conducting experiments and studies to gather reliable data and draw meaningful conclusions. While both groups serve distinct purposes, they possess different attributes that set them apart. In this article, we will explore and compare the attributes of control groups and experimental groups, shedding light on their significance in research.

Control Group

A control group is a group of individuals or subjects in an experiment that does not receive the experimental treatment or intervention. It serves as a baseline against which the experimental group is compared. The primary purpose of a control group is to provide a reference point to measure the effects of the independent variable in the experimental group. By keeping all other variables constant, except for the one being tested, researchers can determine whether the observed changes are due to the intervention or other factors.

One attribute of a control group is that it is randomly selected or assigned. Randomization helps ensure that the control group represents the larger population accurately, reducing the potential for bias. Additionally, the control group should be similar to the experimental group in terms of relevant characteristics such as age, gender, and health status. This similarity allows for a more accurate comparison between the two groups.

Another attribute of a control group is that it receives a placebo or a standard treatment. Placebos are inert substances or procedures that mimic the experimental treatment but have no therapeutic effect. By providing a placebo to the control group, researchers can account for the placebo effect, where individuals may experience improvements simply due to their belief in receiving treatment. Alternatively, the control group may receive a standard treatment that is already established as effective, allowing researchers to compare the experimental treatment against an existing standard.

Control groups are also characterized by their size. The larger the control group, the more reliable the results are likely to be. A larger sample size helps reduce the impact of individual variations and increases the statistical power of the study. It allows for more accurate generalizations and strengthens the validity of the findings.

Lastly, control groups are typically subjected to the same conditions as the experimental group, except for the intervention being tested. This ensures that any observed differences between the two groups can be attributed to the independent variable and not external factors. By controlling the environment and other variables, researchers can isolate the effects of the intervention and draw more accurate conclusions.

Experimental Group

The experimental group, also known as the treatment group, is the group of individuals or subjects in an experiment that receives the experimental treatment or intervention being tested. Unlike the control group, the experimental group is exposed to the independent variable, allowing researchers to assess the effects of the intervention.

One attribute of the experimental group is that it is carefully selected or assigned. Researchers must ensure that the individuals in the experimental group meet specific criteria and are representative of the population being studied. This selection process helps increase the internal validity of the study and enhances the generalizability of the findings.

Another attribute of the experimental group is that it undergoes the experimental treatment or intervention. This treatment can be a new drug, therapy, educational program, or any other intervention being tested. By administering the intervention to the experimental group, researchers can observe and measure its effects, comparing them to the control group's outcomes.

The size of the experimental group is also an important attribute. Similar to the control group, a larger sample size in the experimental group increases the reliability and statistical power of the study. It allows for more accurate assessments of the intervention's effectiveness and helps identify any potential side effects or adverse reactions.

Experimental groups are often subjected to pre and post-tests to measure the changes resulting from the intervention. These tests can include surveys, physical examinations, cognitive assessments, or any other relevant measurements. By comparing the pre and post-intervention results, researchers can determine the impact of the intervention on the dependent variable.

Lastly, experimental groups may be divided into subgroups to explore different variables or conditions. This approach allows researchers to assess the effects of the intervention across various demographics, such as age groups or different levels of severity. By analyzing subgroups within the experimental group, researchers can gain a deeper understanding of how the intervention affects different populations.

Control groups and experimental groups are fundamental components of scientific research. While control groups provide a reference point and help establish causality, experimental groups allow researchers to assess the effects of interventions. Both groups possess distinct attributes that contribute to the validity and reliability of the study. By understanding and comparing the attributes of control groups and experimental groups, researchers can conduct rigorous experiments and generate meaningful insights that advance scientific knowledge.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

Related Posts

Experimental Design: Types, Examples & Methods

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.

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Understanding Experimental Groups

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Scientific experiments often include two groups: the experimental group and the control group . Here's a closer look at the experimental group and how to distinguish it from the experimental group.

Key Takeaways: Experimental Group

• The experimental group is the set of subjects exposed to a change in the independent variable. While it's technically possible to have a single subject for an experimental group, the statistical validity of the experiment will be vastly improved by increasing the sample size.
• In contrast, the control group is identical in every way to the experimental group, except the independent variable is held constant. It's best to have a large sample size for the control group, too.
• It's possible for an experiment to contain more than one experimental group. However, in the cleanest experiments, only one variable is changed.

Experimental Group Definition

An experimental group in a scientific experiment is the group on which the experimental procedure is performed. The independent variable is changed for the group and the response or change in the dependent variable is recorded. In contrast, the group that does not receive the treatment or in which the independent variable is held constant is called the control group .

The purpose of having experimental and control groups is to have sufficient data to be reasonably sure the relationship between the independent and dependent variable is not due to chance. If you perform an experiment on only one subject (with and without treatment) or on one experimental subject and one control subject you have limited confidence in the outcome. The larger the sample size, the more probable the results represent a real correlation .

Example of an Experimental Group

You may be asked to identify the experimental group in an experiment as well as the control group. Here's an example of an experiment and how to tell these two key groups apart .

Let's say you want to see whether a nutritional supplement helps people lose weight. You want to design an experiment to test the effect. A poor experiment would be to take a supplement and see whether or not you lose weight. Why is it bad? You only have one data point! If you lose weight, it could be due to some other factor. A better experiment (though still pretty bad) would be to take the supplement, see if you lose weight, stop taking the supplement and see if the weight loss stops, then take it again and see if weight loss resumes. In this "experiment" you are the control group when you are not taking the supplement and the experimental group when you are taking it.

It's a terrible experiment for a number of reasons. One problem is that the same subject is being used as both the control group and the experimental group. You don't know, when you stop taking treatment, that is doesn't have a lasting effect. A solution is to design an experiment with truly separate control and experimental groups.

If you have a group of people who take the supplement and a group of people who do not, the ones exposed to the treatment (taking the supplement) are the experimental group. The ones not-taking it are the control group.

How to Tell Control and Experimental Group Apart

In an ideal situation, every factor that affects a member of both the control group and experimental group is exactly the same except for one -- the independent variable . In a basic experiment, this could be whether something is present or not. Present = experimental; absent = control.

Sometimes, it's more complicated and the control is "normal" and the experimental group is "not normal". For example, if you want to see whether or not darkness has an effect on plant growth. Your control group might be plants grown under ordinary day/night conditions. You could have a couple of experimental groups. One set of plants might be exposed to perpetual daylight, while another might be exposed to perpetual darkness. Here, any group where the variable is changed from normal is an experimental group. Both the all-light and all-dark groups are types of experimental groups.

Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.

Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN 978-0-471-72756-9.

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