What Is a Control in an Experiment? (Definition and Guide)

What Is a Control in an Experiment?

A control is an experimental condition that provides a baseline for comparison. It does not receive the experimental treatment, or it receives an established standard, placebo or alternative condition.

For example, imagine that researchers want to test whether a new fertilizer increases plant growth. One group of plants receives the fertilizer, while the control group does not. If both groups otherwise receive the same water, light and soil, differences in growth may be associated with the fertilizer.

The control does not prove causation by itself. Researchers must also use appropriate sampling, measurement and analysis methods.

Control vs. Control Group vs. Controlled Variable

These terms are related but should not be used interchangeably.

Control

A control is the baseline condition or reference used for comparison.

Control Group

A control group is a collection of participants, samples or test units assigned to the baseline condition.

In a clinical trial, the control group might receive a placebo, an existing treatment or no treatment. The experimental group receives the intervention being studied.

The NCBI Bookshelf describes a control group as the comparison group that helps investigators determine whether an observed effect results from the experimental intervention or from other factors.

Controlled Variable

A controlled variable is a factor researchers try to keep consistent across all experimental conditions.

In the fertilizer example:

• Fertilizer type is the independent variable.
• Plant growth is the dependent variable.
• Water, light, plant species and soil are controlled variables.
• Plants receiving no fertilizer form the control group.

An experiment may have controlled variables even when it does not have a separate control group.

Variables in an Experiment

Most experiments involve independent, dependent and controlled variables.

Independent Variable

The independent variable is the factor the researcher deliberately changes.

Examples include:

• Fertilizer concentration
• Training method
• Medication dosage
• Website design
• Machine temperature

A clear experiment typically changes one primary independent variable at a time.

Dependent Variable

The dependent variable is the outcome researchers measure.

Examples include:

• Plant height
• Test scores
• Recovery time
• Conversion rate
• Product strength

The dependent variable may change in response to the independent variable.

Controlled Variables

Controlled variables are factors kept as consistent as possible.

Examples include:

• Testing duration
• Room temperature
• Equipment
• Sample size
• Measurement method
• Participant instructions

Controlled variables reduce the likelihood that another factor explains the observed result.

Confounding Variables

A confounding variable is an outside factor associated with both the independent variable and the outcome. It can create a misleading relationship if it is not addressed.

Researchers may use randomization, matching, statistical adjustment or design restrictions to reduce confounding.

Types of Controls

Different experiments require different control conditions.

Negative Control

A negative control is expected not to produce the effect being tested.

For example, plants receiving no fertilizer can serve as a negative control when testing a growth treatment. If the negative control unexpectedly changes, the researcher may need to investigate contamination or procedural problems.

Positive Control

A positive control uses a condition already known to produce an effect.

For example, researchers testing a new disinfectant might compare it with an established disinfectant. If the positive control fails, there may be a problem with the procedure or measurement.

Placebo Control

A placebo resembles the experimental treatment but lacks its active component. Placebos can help researchers distinguish treatment effects from participants’ expectations.

Placebo controls are associated with human research and require appropriate ethical oversight.

Active Control

An active control receives an established treatment. Researchers can compare a new intervention with the current standard rather than with no treatment.

No-Treatment Control

A no-treatment group does not receive the experimental intervention. Researchers continue to observe and measure this group during the study.

Historical or External Control

A historical control uses data collected from a previous study or outside population.

External controls may be useful when a concurrent control is impractical or unethical. However, differences in participants, time periods and measurement methods can make the comparison less reliable.

Experimental Group vs. Control Group

The experimental group receives the independent variable or intervention being tested. The control group receives the baseline condition.

For a valid comparison, researchers try to make the groups similar except for the intervention.

In randomized research, participants are assigned to groups by chance. Randomization can reduce selection bias and help distribute known and unknown characteristics across groups. It does not guarantee that the groups will be identical, particularly in small samples.

Why Are Controls Important?

Controls help researchers:

• Establish a baseline
• Identify the effect of an intervention
• Detect procedural errors
• Separate treatment effects from natural changes
• Reduce alternative explanations
• Evaluate measurement accuracy
• Replicate the experiment
• Communicate results more clearly

Without a meaningful comparison, researchers may observe a change but be unable to determine why it occurred.

How To Develop a Control for an Experiment

Use the following steps to design an appropriate control.

1. Define the Research Question

Write a specific question describing what you want to test.

For example:

Does adding 10 grams of fertilizer per week increase the average height of tomato plants over six weeks?

This question identifies the intervention, measurement and time period.

2. Develop a Testable Hypothesis

A hypothesis predicts the relationship between the independent and dependent variables.

For example:

Tomato plants receiving 10 grams of fertilizer per week will grow taller than plants receiving no fertilizer.

A good hypothesis can be evaluated using observable data.

3. Identify the Independent Variable

Determine the factor you intend to change. In the example, the independent variable is fertilizer treatment.

If you test several fertilizer amounts, each dosage becomes a level of the independent variable.

4. Identify the Dependent Variable

Choose an outcome that can be measured consistently. Plant height may be measured in centimeters at the same time each week.

Avoid vague outcomes such as “plants look healthier” unless you define a reliable rating method.

5. Identify Controlled Variables

List other factors that could influence the outcome.

For the plant experiment, keep the following consistent:

• Plant species and approximate age
• Soil type and quantity
• Pot size
• Water amount
• Light exposure
• Temperature
• Measurement schedule

Perfect control is rarely possible, but careful procedures can reduce unnecessary variation.

6. Select the Control Condition

Choose the most appropriate baseline. In this example, the control plants receive no fertilizer while all other procedures remain the same.

In another experiment, the correct control might be an existing product, placebo or standard process.

7. Assign the Test Units

Assign participants, samples or objects to the experimental and control conditions. Random assignment is useful when practical and ethical.

Document the method so another researcher can understand how the groups were created.

8. Conduct the Experiment Consistently

Follow the same procedure for every group except for the planned difference in the independent variable.

Use consistent:

• Instructions
• Equipment
• Timing
• Environment
• Data collection
• Measurement techniques

9. Record and Analyze the Results

Compare the dependent-variable measurements for the experimental and control conditions.

Analysis may involve:

• Averages
• Percentages
• Graphs
• Differences between groups
• Confidence intervals
• Statistical tests

The appropriate method depends on the research question and data.

10. Draw a Cautious Conclusion

Determine whether the results support the hypothesis. Consider alternative explanations, measurement limitations and sample size.

A difference between groups may reflect the intervention, chance, bias or an uncontrolled factor. The conclusion should reflect the strength of the evidence.

Example of a Control in a Plant Experiment

A researcher wants to determine whether a new fertilizer improves plant growth.

The experimental design includes:

• Independent variable: Fertilizer treatment
• Dependent variable: Plant height after six weeks
• Control group: Plants receiving no fertilizer
• Experimental group: Plants receiving the new fertilizer
• Controlled variables: Water, soil, light, temperature, pot size and plant species

If the treated plants grow more, the fertilizer may have contributed to the difference. Repeated trials can help determine whether the result is consistent.

Example of a Control in Business Testing

A company wants to test whether a redesigned landing page increases registrations.

The original page serves as the control, while the redesigned page is the experimental condition.

The company randomly sends visitors to one version and compares registration rates. Factors such as traffic source, device and campaign timing should be monitored because they may affect the result.

This type of experiment is commonly called an A/B test.

Example of a Control in Product Testing

A manufacturer wants to determine whether a new production process improves product durability.

Products made with the existing process form the control group. Products made with the new process form the experimental group.

Both groups should use comparable materials and undergo the same durability test. This helps isolate the effect of the production process.

Common Control-Design Mistakes

Confusing the Control Group With Controlled Variables

A control group is the baseline comparison. Controlled variables are the conditions kept consistent across groups.

Changing Multiple Factors

If researchers change several variables at once, they may not know which one caused the result.

Using Groups That Are Not Comparable

Major differences between the experimental and control groups can introduce bias.

Measuring Groups Differently

Using different equipment, instructions or timing can create artificial differences.

Using an Inappropriate Baseline

The control should answer the research question. A no-treatment group may not be appropriate when the objective is to compare a new process with the existing standard.

Ignoring Ethical Requirements

Some experiments cannot ethically withhold an established treatment or expose participants to unnecessary risk. Human and animal research may require formal review and informed consent.

Careers That Use Experimental Controls

Professionals who may design or interpret controlled experiments include:

• Laboratory scientists
• Medical researchers
• Agricultural researchers
• Data analysts
• Product managers
• Software developers
• Quality assurance specialists
• Engineers
• Market researchers
• Psychologists
• Public health professionals
• Manufacturing specialists

The exact standards vary by industry and the level of risk involved.

Present Experimental Results Clearly With Dokie

Experimental results often include hypotheses, variables, procedures, charts and conclusions that can be difficult to explain in a text-only report. Dokie can turn research documents, datasets, URLs and structured notes into a professional presentation with a logical visual narrative.

Dokie is an AI presentation maker that supports custom templates and editable PPTX export. Researchers and business teams can use it to prepare experiment summaries, A/B test reports, product evaluations and stakeholder updates, then refine the slides with their own methodology, charts and limitations.

Frequently Asked Questions

Does every experiment need a control group?

No. Some experiments compare different levels of an independent variable without a separate untreated group. However, experiments generally need controlled variables and a meaningful basis for comparison.

What is the difference between a control and a constant?

A control is a baseline condition used for comparison. A constant, or controlled variable, is a factor kept consistent throughout the experiment.

Can a control group receive treatment?

Yes. An active control group may receive an established treatment, while the experimental group receives the new treatment being evaluated.

Why is random assignment important?

Random assignment can reduce selection bias and make experimental and control groups more comparable at the beginning of a study. It strengthens the ability to associate differences with the tested intervention.