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An experimental study is a standard data collection method where samples are manipulated by applying a treatment before data collection. Researchers deliberately modify one variable to determine its effect on another variable. The samples subjected to treatment are called experimental units. This approach allows researchers to establish cause-and-effect relationships between variables.

Control groups serve as a comparison baseline in experiments. While the experimental group receives a specific treatment, the control group may receive a placebo or no treatment. This comparison allows researchers to isolate the treatment's true effect. Clinical trials commonly use this approach, where one group receives the drug and the control group receives a placebo to evaluate efficacy accurately.

Clinical trials are typical examples of experimental data collection. Before releasing a drug for public use, researchers test its efficacy on randomly selected volunteer groups. One group receives specific drug doses while a control group receives a placebo. Researchers then monitor subjects for weeks, observe disease symptoms and side effects, and collect data to determine the drug's effectiveness and safety.

Researchers can study fertilizer effects on plant growth using experimental methods. Plants are divided into groups, with some receiving the new fertilizer treatment and others serving as controls. Growth is monitored daily over weeks, and data is collected on plant heights and development. This manipulation of the fertilizer variable allows researchers to determine its direct impact on plant growth outcomes.

Random selection ensures that experimental and control groups are representative and unbiased. In clinical trials, volunteers are randomly assigned to receive either the treatment or placebo. This randomization minimizes selection bias and ensures that observed differences result from the treatment itself rather than pre-existing group differences, strengthening the validity of conclusions drawn from the data.

Experimental studies collect data that can be how data are classified categorical data or how data are classified numerical data. For example, clinical trials might collect numerical data on blood pressure readings or categorical data on symptom presence or absence. The type of data collected depends on what researchers measure to evaluate the treatment's effect on the outcome variable.

Experimental data collection involves actively manipulating variables and applying treatments to samples before measuring outcomes. Unlike data collection by observations, which passively records natural phenomena, experiments impose controlled conditions. This active manipulation allows researchers to establish causal relationships and test specific hypotheses about how one variable affects another.