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Key Takeaways
- An experimental group receives the treatment or intervention under study, while a control group does not, so researchers can isolate the effect of the independent variable.
- Modern studies often use more than 1 experimental group, factorial designs, and blinding procedures to strengthen validity beyond a simple 2-group comparison.
- Sample size, homogeneity, random assignment, and blinding all affect how confidently a researcher can attribute results to the treatment rather than to chance or bias.
- Quasi-experiments use experimental groups without full random assignment, which is common in education and social science research where randomization is not feasible.
Contents
- Glossary of Key Terms
- What Is an Experimental Group?
- How Does an Experimental Group Differ From a Control Group?
- What Types of Experimental Groups Exist?
- Between-Subjects, Within-Subjects, and Mixed Designs
- Why Does Blinding Matter in Experimental Groups?
- True Experiments Versus Quasi-Experiments
- Randomization Between Experimental and Control Groups
- How to Match Experimental and Control Groups
- What Threatens the Validity of an Experimental Group?
- Does Homogeneity Always Help an Experimental Group?
- How the Experimental Group Affects Internal and External Validity
- How Large Should an Experimental Group Be?
- Analyzing Data From Experimental Groups
- Characteristics of an Effective Experimental Group
- Examples of Experimental Groups in Research
- Frequently Asked Questions
- References
Glossary of Key Terms
| Term | Definition |
| Experimental group | The group of participants or units that receives the treatment or intervention being studied. |
| Control group | The group that does not receive the treatment, used as a baseline for comparison. |
| Independent variable | The factor a researcher deliberately changes or manipulates in an experiment. |
| Dependent variable | The outcome a researcher measures to see if it was affected by the independent variable. |
| Random assignment | Placing participants into groups by chance so that each group is comparable at the start. |
| Blinding | Withholding information about group assignment from participants, researchers, or data analysts to reduce bias. |
| Confounding variable | An outside factor that affects the outcome and makes it hard to isolate the treatment’s true effect. |
| Between-subjects design | A design in which different participants are assigned to different groups. |
| Within-subjects design | A design in which the same participants take part in every condition. |
| Factorial design | A design that tests 2 or more independent variables at once, in combination. |
| Quasi-experiment | A study that compares an experimental group and a control group without full random assignment. |
| Statistical power | The probability that a study will detect a true effect if one exists. |
What Is an Experimental Group?
An experimental group is the set of participants, samples, or units in an experimental study that receives the treatment, intervention, or condition a researcher wants to test. It is compared against a control group that does not receive the treatment.
For example, in a drug trial the experimental group takes the new medication, in an education study the experimental group learns with a new teaching method, and in an agricultural trial the experimental group receives a new fertilizer. In each case, the researcher measures a dependent variable, such as recovery rate, test score, or plant growth, to see whether the treatment made a difference.
How Does an Experimental Group Differ From a Control Group?
An experimental group receives the treatment being tested, while a control group does not, so researchers can compare outcomes and isolate the treatment’s effect. Any difference between the 2 groups is attributed to the treatment, provided the groups were otherwise similar to begin with.
Control Group Versus Experimental Group at a Glance
| Feature | Experimental Group | Control Group |
| Receives treatment | Yes | No |
| Purpose | Shows the effect of the treatment | Provides a baseline for comparison |
| Assignment | Random, where possible | Random, where possible |
| Example (drug trial) | Takes the new drug | Takes a placebo or standard care |
What Types of Experimental Groups Exist?
A study can use a single experimental group, several experimental groups at once, or a factorial design that combines more than 1 independent variable. The right choice depends on the research question and the number of conditions a researcher needs to compare.
Single Experimental Group Designs
The simplest design compares 1 experimental group with 1 control group. This works well for a straightforward question, such as whether a single new drug outperforms a placebo, but it cannot show how different doses or versions of a treatment compare with each other.
Multiple Experimental Groups
Many studies use 2 or more experimental groups to compare different levels or versions of a treatment against 1 control group. Common examples include:
- A low-dose group, a high-dose group, and a placebo group in a drug trial.
- 3 different teaching methods compared against a standard curriculum in an education study.
- Multiple fertilizer concentrations tested against an untreated plot in agriculture.
Factorial Designs
A factorial design tests 2 or more independent variables at the same time, creating a separate group for every combination. A 2×2 factorial design, for instance, has 4 groups, which lets researchers study each variable on its own and also test whether the variables interact.
| Group | Variable A | Variable B |
| Group 1 | Drug present | Exercise present |
| Group 2 | Drug present | Exercise absent |
| Group 3 | Drug absent | Exercise present |
| Group 4 (control) | Drug absent | Exercise absent |
Between-Subjects, Within-Subjects, and Mixed Designs
Beyond how many groups a study uses, researchers must decide whether each participant experiences only 1 condition or all conditions. This choice shapes how the experimental group is built and analyzed.
Between-Subjects Design
In a between-subjects design, different participants are randomly assigned to the experimental group or the control group, and each person experiences only 1 condition. This avoids carryover effects but requires more participants overall and relies on random assignment to keep the groups comparable.
Within-Subjects Design
In a within-subjects design, the same participants take part in every condition, often in a randomized order, and act as their own control. This reduces the number of participants needed and controls for individual differences, but it can introduce order effects, such as fatigue or practice.
Mixed Designs
A mixed design combines both approaches: 1 variable is tested between different groups, while another is tested within the same participants. This is common in longitudinal studies that compare an experimental group and a control group at several points in time.
Why Does Blinding Matter in Experimental Groups?
Blinding matters because it stops participants, researchers, or data analysts from unconsciously changing behavior, treatment, or scoring based on who is in the experimental group. Without blinding, expectations alone can produce a difference between groups that has nothing to do with the actual treatment.
Single-Blind Studies
In a single-blind study, only the participants do not know whether they are in the experimental group or the control group. This reduces bias caused by participant expectations but does not stop the researcher from unintentionally influencing results.
Double-Blind Studies
In a double-blind study, neither the participants nor the researchers running the sessions know who is in the experimental group. This is considered the gold standard in clinical research because it limits bias from both sides at once.
Triple-Blind Studies
A triple-blind study extends blinding to the data analysts as well, so the people who process and interpret the results also do not know which participants were in the experimental group until after the analysis is complete.
| Design | Participants Unaware | Researchers or Analysts Unaware |
| Single-blind | Yes | No |
| Double-blind | Yes | Yes (researchers) |
| Triple-blind | Yes | Yes (researchers and analysts) |
True Experiments Versus Quasi-Experiments
A true experiment uses random assignment to place participants into the experimental group or the control group, which keeps the groups balanced on both known and unknown factors. This is the strongest design for showing that a treatment caused an outcome.
A quasi-experiment compares an experimental group and a control group without full random assignment, often because the groups already exist, such as 2 separate classrooms or 2 hospital wards. Quasi-experiments are common in education, social science, and public health research, where randomization is impractical or unethical, but they make it harder to rule out pre-existing differences between the groups.
Experimental Group Across Different Study Designs
Note: only true experiments have an “experimental group” in the strict sense. Observational designs (correlational, cross-sectional, cohort, case-control) don’t assign treatment, so the concept applies loosely, if at all. The table below reflects that distinction.
| Study Design | Does It Have a True Experimental Group? | What Plays a Similar Role |
| Correlational | No | There is no treatment assignment at all; researchers simply measure 2 or more variables as they naturally occur and look for a relationship between them. |
| Cross-sectional | No | Researchers measure exposure and outcome at a single point in time across a population; groups are defined by existing characteristics (e.g., smokers vs. non-smokers), not by assigned treatment. |
| Cohort | No | Participants are grouped by exposure status they already have (e.g., exposed vs. unexposed to a risk factor) and followed forward in time; the “exposed” group is sometimes loosely called the experimental group, but it was not randomly assigned. |
| Case-control | No | Groups are defined by outcome status (cases who have the disease vs. controls who do not), and researchers look backward for prior exposure; there is no assigned experimental group. |
| Randomized clinical trial | Yes | Participants are randomly assigned to receive the treatment (true experimental group) or a placebo/standard care (control group), making this the only design in the table with a genuine, randomly assigned experimental group. |
Key takeaway: the term “experimental group” strictly applies only to designs with researcher-controlled, randomly assigned treatment, which in this list is just the randomized clinical trial. In the observational designs, what looks like an “experimental group” is really just a naturally occurring exposure or outcome group, and this distinction matters because it limits how confidently researchers can claim the exposure caused the outcome.
Randomization Between Experimental and Control Groups
What is randomization?
Randomization is the process of assigning participants to the experimental group or the control group purely by chance, so that neither the researcher nor the participant has any influence over which group a person ends up in. It is the single most powerful tool for making groups comparable before treatment begins.
Why randomization matters
Randomization balances both known and unknown characteristics across groups. Matching can only balance the variables a researcher thinks to measure, but randomization protects against imbalance in factors nobody anticipated, including ones that were never even measured.
Common Randomization Methods
- Simple randomization. Each participant is assigned using a method equivalent to a coin flip, such as a random number generator; straightforward, but can produce unequal group sizes or chance imbalances in small samples.
- Block randomization. Participants are randomized in small, fixed-size blocks (e.g., blocks of 4) to ensure the experimental and control groups stay roughly equal in size throughout the enrollment period.
- Stratified randomization. Participants are first divided into subgroups (strata) based on a key characteristic, such as age group or disease severity, and randomization happens separately within each stratum, ensuring balance on that characteristic.
- Cluster randomization. Whole groups, such as schools, clinics, or villages, are randomized together rather than individuals; used when treating individuals separately within the same setting is impractical or would contaminate the control group.
What Randomization Does and Does Not Do
| Randomization | Does | Does Not |
| Effect | Balances groups on average, across many characteristics at once | Guarantee perfect balance in every single study, especially with small samples |
| Scope | Removes selection bias in how participants enter a group | Remove bias introduced after assignment, such as unblinded observer bias |
| Requirement | Needs a true experiment, where the researcher controls assignment | Apply to quasi-experiments, where groups already exist |
Common Pitfalls
- Confusing random assignment with random sampling. Random sampling is about how participants are selected from a population; random assignment is about how those participants are then placed into groups. A study can have one without the other.
- Small sample sizes. With few participants, simple randomization can still produce a lopsided split by chance; block or stratified randomization helps guard against this.
- Breaking allocation concealment. If researchers can predict or see upcoming assignments before enrolling a participant, they may unconsciously (or consciously) influence who enters the study, undermining the randomization.
How to Match Experimental and Control Groups
What is matching?
Matching is the process of making the experimental group and the control group as similar as possible on everything except the treatment, so that any difference in outcome can be attributed to the treatment rather than to pre-existing group differences.
Why match groups at all?
Even with random assignment, small samples can end up unbalanced on important characteristics by chance. Matching, used alongside or instead of randomization, reduces this risk and strengthens internal validity.
Common Matching Methods
- Random assignment. The default method in a true experiment; each participant has an equal chance of landing in either group, which balances both known and unknown characteristics over a large enough sample.
- Individual (pairwise) matching. Each participant in the experimental group is paired with a participant in the control group who shares key characteristics, such as age, sex, or baseline severity, before treatment is assigned.
- Frequency (stratified) matching. Instead of pairing individuals, researchers ensure both groups have the same overall proportions of a characteristic, for example, an equal percentage of participants over age 50 in each group.
- Propensity score matching. Used often in observational or quasi-experimental designs; a single score is calculated for each participant based on multiple characteristics, and participants with similar scores are matched across groups.
What to Match On
- Demographic factors (age, sex, socioeconomic status)
- Baseline severity or starting values of the response variable
- Known confounding variables specific to the study topic (e.g., smoking status in a respiratory drug trial)
- Setting or timing factors (same clinic, same season) when these could plausibly affect outcomes
Common Pitfalls
- Overmatching. Matching on a variable that is actually part of the causal pathway between the treatment and the outcome can hide a real effect; only match on variables that affect the outcome independently of the treatment.
- Matching on too many variables. This can make it hard to find suitable matches, shrinking the usable sample size.
- Assuming matching replaces randomization. Matching balances the variables researchers choose to match on, but only random assignment protects against imbalance in variables nobody thought to measure.
Bottom line: randomization is the strongest tool for creating comparable groups, and matching is a useful supplement, or a necessary substitute, when full randomization is not possible, as in many quasi-experimental designs.
What Threatens the Validity of an Experimental Group?
Several factors can make it look like a treatment worked when it did not, or hide a real effect. Researchers try to control for these threats when designing and running an experimental group.
What Is a Confounding Variable?
A confounding variable is an outside factor, other than the treatment, that differs between the experimental group and the control group and affects the outcome being measured. If age, diet, or prior experience differs systematically between groups, it can be mistaken for a treatment effect.
What Is the Hawthorne Effect?
The Hawthorne effect is a change in behavior that occurs simply because participants know they are being observed or are part of a study. It can make an experimental group perform better regardless of the actual treatment, which is 1 reason blinding is used.
Placebo Effect
The placebo effect occurs when participants in the control group improve simply because they believe they are receiving a real treatment. Comparing the experimental group with a placebo-treated control group, rather than an untreated one, helps separate the treatment’s true effect from this expectation-driven improvement.
Regression to the Mean
Regression to the mean is the tendency for extreme scores to move closer to average on a second measurement, even without any treatment. If participants were selected for an experimental group because their initial scores were unusually high or low, some of the apparent improvement may simply reflect this statistical pattern.
Does Homogeneity Always Help an Experimental Group?
No. A homogeneous experimental group, meaning participants who are similar on key characteristics, reduces unwanted variation and makes it easier to detect a treatment effect. However, this comes at a cost: results from a very homogeneous group, such as young, healthy adults, may not generalize to the wider population the treatment is meant to serve.
Researchers balance this trade-off by defining clear inclusion and exclusion criteria for the experimental group, and by noting in their results how far the findings can reasonably be generalized. Some studies deliberately include a more diverse sample once an initial homogeneous trial shows promising results.
How the Experimental Group Affects Internal and External Validity
What is internal validity?
Internal validity asks whether the treatment, and nothing else, caused the observed difference between the experimental group and the control group. How the experimental group is built directly controls this:
- Random assignment to the experimental group balances known and unknown participant differences, which is the single biggest lever on internal validity.
- Blinding the experimental group’s participants and researchers removes bias from expectations and observer behavior.
- A tightly controlled, homogeneous experimental group reduces noise from outside factors, making a real treatment effect easier to detect.
What is external validity?
External validity asks whether the result from the experimental group would hold true for people, settings, or conditions outside the study. This is where the same choices that help internal validity often hurt external validity:
- A narrow, homogeneous experimental group (e.g., only young, healthy adults) boosts internal validity but limits how far the finding can be generalized.
- A heavily controlled lab setting for the experimental group increases certainty about cause and effect, but may not reflect real-world conditions where the treatment will actually be used.
- A quasi-experimental group, drawn from an existing, more diverse population, often has weaker internal validity but stronger external validity than a tightly randomized lab sample.
In short, tightening control over the experimental group tends to strengthen internal validity while narrowing external validity, and researchers have to decide where to sit on that trade-off based on their study’s goal.
How Large Should an Experimental Group Be?
The right size depends on the expected effect size, the variability of the outcome, and how confident the researcher needs to be in the result. Both the experimental group and the control group typically need enough participants to detect a real difference if 1 exists.
Sample Size
Sample size is the number of participants or units in each group. Too small a sample can miss a real effect, while an unnecessarily large sample wastes resources and, in a clinical trial, may expose more participants than needed to an unproven treatment.
Statistical Power
Statistical power is the probability that a study will detect a true treatment effect if 1 exists, and it is usually set at a target of 80% or higher before a study begins. A power analysis, done before data collection, estimates how many participants each experimental and control group needs based on the expected effect size.
Analyzing Data From Experimental Groups
Once data collection ends, researchers compare the experimental group and the control group statistically, using various inferential tests, rather than by eye. Common approaches include:
- A t-test, used to compare the means of 2 groups, such as 1 experimental group and 1 control group.
- Analysis of variance (ANOVA), analysis of covariance (ANCOVA), or multivariate analysis of variance (MANOVA), used to compare 3 or more groups at once, such as multiple experimental groups and 1 control group.
- A p-value, which indicates how likely the observed difference is if the treatment actually had no effect.
- A confidence interval, which gives a range of plausible values for the true size of the treatment effect.
A result is usually called statistically significant when the p-value falls below a threshold set in advance, commonly 0.05, though significance alone does not confirm that the effect size is large or practically meaningful.
Characteristics of an Effective Experimental Group
- Random assignment, so the experimental and control groups are comparable at the start of the study.
- Adequate, power-calculated sample size, so real effects are not missed.
- Controlled isolation of the independent variable, with confounding variables minimized or measured.
- Appropriate blinding, where feasible, to reduce bias from expectations.
- Consistent, well-documented data collection procedures across every group.
- Replicability, meaning another researcher could repeat the study and expect similar results.
Examples of Experimental Groups in Research
| Field | Experimental Group Receives | Control Group Receives |
| Medicine | New drug or dosage | Placebo or standard treatment |
| Education | New teaching method | Standard curriculum |
| Agriculture | New fertilizer or irrigation method | No treatment or standard practice |
| Psychology | New therapy technique | Standard therapy or waitlist |
Frequently Asked Questions
What is the difference between an experimental group and a control group in an experiment?
The experimental group receives the treatment or intervention being tested, while the control group does not. Comparing outcomes between the 2 lets researchers attribute any difference to the treatment rather than to chance.
Can a study have more than 1 experimental group?
Yes. Many studies use several experimental groups, for example a low-dose and a high-dose group, or use a factorial design with a separate group for every combination of variables being tested.
What is a double-blind experimental group study?
A double-blind study is one in which neither the participants nor the researchers running the sessions know who is in the experimental group and who is in the control group, which reduces bias from both sides.
How many participants should be in an experimental group for statistical significance?
There is no fixed number. Researchers run a power analysis before the study, based on the expected effect size and variability, to estimate the minimum sample size needed in each experimental and control group.
What is a quasi-experimental design and how does it use experimental groups?
A quasi-experimental design compares an experimental group and a control group without full random assignment, often because the groups already exist, such as separate schools or clinics. It is common when randomization is impractical or unethical.
What is the difference between between-subjects and within-subjects experimental groups?
In a between-subjects design, different participants are assigned to the experimental group or the control group. In a within-subjects design, the same participants experience every condition, acting as their own control.
Why is random assignment important for experimental and control groups?
Random assignment spreads both known and unknown participant differences evenly across the experimental and control groups. This makes it far more likely that any outcome difference is due to the treatment rather than pre-existing group differences.
What is a confounding variable in an experiment?
A confounding variable is an outside factor that differs between the experimental group and the control group and also affects the outcome, making it hard to tell whether the treatment or the confound caused the result.
What is the difference between an explanatory variable and a response variable?
The explanatory variable is the presumed cause, and the response variable is the presumed effect. In a drug trial, dosage is the explanatory variable, while recovery rate is the response variable.
References
- National Cancer Institute. Dictionary of Cancer Terms: Experimental Group. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/experimental-group
- Yale University Department of Statistics. Introduction to Experimental Design. http://www.stat.yale.edu/Courses/1997-98/101/expdes.htm
- University of Edinburgh, Biomedical Sciences. Experimental Design and Data Analysis: What to Do With Experiments, Chapter 9. https://biomedical-sciences.ed.ac.uk/experimental-design-and-data-analysis/what-to-do-with-experiments/chapter-9
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