An experimental group is the group in a study that receives the treatment, intervention, or condition being tested. It’s the group researchers actively change something about, so they can measure whether that change has an effect. Every controlled experiment needs at least one experimental group and one control group to produce meaningful results.
How the Experimental Group Works
In any experiment, researchers want to know whether a specific change causes a specific outcome. The experimental group is where that change happens. If scientists are testing whether a new walking program improves heart health, the experimental group is the one that starts walking. If a psychologist wants to know whether diversity training changes workplace behavior, the experimental group is the one that goes through the training.
The thing being changed is called the independent variable. The experimental group is exposed to it; the control group is not. The control group either receives no intervention at all, gets a placebo, or simply continues their normal routine. By comparing the two groups afterward, researchers can isolate whether the independent variable actually caused any difference in outcomes, rather than something else being responsible.
Experimental Group vs. Control Group
The distinction is straightforward: the experimental group gets the intervention, and the control group doesn’t. Consider a study on exercise and blood pressure. Researchers recruit participants and split them into two groups. The experimental group begins a structured program of walking and virtual strength-training classes. The control group is asked to maintain their current lifestyle, exercising less than 90 minutes per week. After the study period, the researchers compare blood pressure changes between the two groups.
Without a control group, there’s no way to know whether any improvement in the experimental group came from the intervention itself or from something unrelated, like seasonal changes, the placebo effect, or simply the passage of time. The control group provides that critical baseline for comparison.
Why Random Assignment Matters
For results to be trustworthy, participants need to be assigned to the experimental or control group at random, not by choice. Random assignment means every participant has an equal chance of landing in either group, similar to a coin flip. Neither the researcher nor the participant gets to pick.
This matters because it creates groups that start out roughly equivalent. If researchers let people volunteer for the experimental group, the kind of person who volunteers might already be different in ways that skew the results. Maybe they’re more motivated, healthier, or younger. Random assignment spreads those differences evenly across both groups, so any difference that shows up later can more confidently be attributed to the intervention itself. The Office of Research Integrity considers following random assignment protocol essential to maintaining the integrity of a study.
Experiments With Multiple Groups
Not every experiment has just one experimental group. When researchers want to understand how much of something works best, they use a multiple-group design with several experimental groups, each receiving a different level of the independent variable.
A simple two-group design (one experimental, one control) tells you whether something has any effect. A multiple-group design tells you how much effect each level has. For example, to find the optimal amount of sleep for exercise performance, researchers might assign participants to groups sleeping four, six, eight, or ten hours per night. This reveals not just that sleep matters, but how much sleep works best, and whether too much sleep actually hurts performance.
The same logic applies to medication dosing. Rather than simply testing whether a drug works, researchers create multiple experimental groups receiving different doses to find the amount that achieves the desired outcome without unnecessary side effects. In behavioral research, multiple groups help rule out confounding variables. A study on whether dance classes relieve depression might include one group that dances, one group that socializes without dancing, and one control group. The social-only group helps determine whether any improvement comes from the dancing itself or just from being around other people.
Classic Examples in Psychology
Some of the most well-known experiments in history illustrate how experimental groups function in practice. In Albert Bandura’s Bobo Doll Experiment in 1961, 72 children between ages 3 and 6 were split into three groups of 24. One group watched adults behaving aggressively toward an inflatable doll, one watched adults playing calmly, and one served as the control with no adult model at all. The group exposed to aggressive behavior was the experimental group, and those children went on to imitate the aggression they’d witnessed.
In Solomon Asch’s 1951 conformity study, 50 male college students participated in what they were told was a vision test, choosing which line on a card was longest. The real experimental manipulation was that the other “participants” in the room were actors who deliberately chose wrong answers. The actual subjects were the experimental group, being tested on whether social pressure would cause them to give an answer they knew was incorrect.
In Stanley Milgram’s 1963 obedience experiment, 40 men were assigned as “teachers” who believed they were administering electric shocks to “learners” (who were actually actors). The teachers were the experimental group, placed under pressure from an authority figure to continue shocking someone in apparent pain. The study revealed how far ordinary people would go when directed by an authority, with a striking majority continuing to the highest voltage levels.
How Results Are Compared
Once an experiment is complete, researchers use statistical tests to determine whether the difference between the experimental group and the control group is real or just due to chance. The most common tools for this are t-tests, which compare a measurable outcome between two groups, and ANOVA (analysis of variance), which does the same thing when there are three or more groups. Chi-square tests are used when the outcome is a category (like “improved” vs. “did not improve”) rather than a number.
The size of each group affects how reliable these comparisons are. Several factors determine how many participants an experimental group needs: how small a difference the researchers are trying to detect (smaller differences require more people), how confident they want to be in the result, and what kind of statistical test they plan to use. A study looking for a subtle effect needs far more participants than one testing something with a dramatic, obvious impact. Underpowered studies, those with too few participants, risk missing real effects or producing results that don’t hold up when repeated.
What Makes an Experimental Group Valid
An experimental group only produces useful data when the experiment is designed well. The group needs to be randomly assigned, large enough for the statistical analysis being used, and treated identically to the control group in every way except the one variable being tested. If the experimental group gets more attention from researchers, meets in a nicer facility, or is assessed at different times than the control group, those differences become alternative explanations for any result.
In medical and clinical research, the experimental group is sometimes called the intervention group or investigational group. The concept is the same regardless of the field: it’s the group that experiences the one thing the researchers are curious about, while everything else is held constant. That simple structure, changing one thing and measuring what happens, is the foundation of the scientific method.