In practice, therefore, researchers often control for observed characteristics that might differ between individuals in the treatment and control groups when estimating treatment effects. However, some differences might occur due to chance. Randomized experiments (also known as RCT or randomized control trials) are considered to be the most rigorous approach, or the РІР‚Сљgold standard,РІР‚Сњ to identifying causal effects because they theoretically eliminate all preexisting differences between the treatment and control groups. Random assignment ensures that all participants have the same chance of being in a given experimental condition. The key distinction between randomized experiments and quasi-experiments lies in the fact that in a randomized experiment, participants are randomly assigned to either the treatment or the control group whereas participants are not in a quasi-experiment. The group of participants that receives the intervention or treatment is known as the 'treatment group,' and the group that does not is known as the РІР‚Сљcontrol groupРІР‚Сњ in randomized experiments and РІР‚Сљcomparison groupРІР‚Сњ in quasi-experiments.

The researchers might also manipulate the value of the child care subsidies in order to determine if higher subsidy values might result in different levels of maternal employment. They could then design an experiment in which some mothers would be provided the option of government-funded child care subsidies and others would not. They might hypothesize that the provision of government-subsidized child care would promote such employment.

Suppose, for example, a group of researchers was interested in the effect of government-funded child care subsidies on maternal employment. The researcher may manipulate whether research subjects receive a treatment (e.g., attendance in a Head Start program: yes or no) or the level of treatment (e.g., hours per day in the program). It addresses the question РІР‚Сљif we change X (the treatment or intervention), what happens to Y (the outcome)?РІР‚Сњ Conducted both in the laboratory and in real life situations, experiments are powerful techniques for evaluating cause-and-effect relationships.

An experiment is a study in which the researcher manipulates the treatment, or intervention, and then measures the outcome.

Causality in research