What Is a Controlled Experiment? | Definitions & Examples
In experiments, researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment, all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.
Controlling variables can involve:
- holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
- measuring variables to statistically control for them in your analyses.
- balancing variables across your experiment through randomization (e.g., using a random order of tasks).
Why does control matter in experiments?
Control in experiments is critical for internal validity, which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases, particularly ones related to issues with generalizability (like sampling bias and selection bias.)
Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.
Methods of control
You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.
Other extraneous variables can be controlled through your sampling procedures. Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).
By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.
After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.
Controlled experiments require control groups. Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect), and compare the outcome with your experimental treatment.
You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.
This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups.
Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study, from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias.
Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses, leading to observer bias. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics. If participants behave a particular way due to awareness of being observed (called a Hawthorne effect), your results could be invalidated.
Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.
Problems with controlled experiments
Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.
Difficult to control all variables
Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.
But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.
Risk of low external validity
Controlled experiments have disadvantages when it comes to external validity—the extent to which your results can be generalized to broad populations and settings.
The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.
Other interesting articles
Frequently asked questions about controlled experiments
- What are the requirements for a controlled experiment?
- A control group that receives a standard treatment, a fake treatment, or no treatment.
- Random assignment of participants to ensure the groups are equivalent.
Depending on your study topic, there are various other methods of controlling variables.
- What is the difference between a control group and an experimental group?
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.
- What is experimental design?
Experimental design means planning a set of procedures to investigate a relationship between variables. To design a controlled experiment, you need:
- A testable hypothesis
- At least one independent variable that can be precisely manipulated
- At least one dependent variable that can be precisely measured
When designing the experiment, you decide:
- How you will manipulate the variable(s)
- How you will control for any potential confounding variables
- How many subjects or samples will be included in the study
- How subjects will be assigned to treatment levels
Experimental design is essential to the internal and external validity of your experiment.
Cite this Scribbr article
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.