* Manipulation and Control: Experimental studies involve manipulating an independent variable (the presumed cause) and observing its effect on a dependent variable (the presumed effect). Researchers carefully control other factors (confounding variables) that might influence the outcome, minimizing alternative explanations for the observed results. This control is a key differentiator from other research methods.
* Random Assignment: Ideally, participants are randomly assigned to different groups (e.g., treatment and control groups). Random assignment ensures that the groups are comparable at the beginning of the study, reducing the likelihood that pre-existing differences between groups account for any observed differences after the manipulation.
* Stronger Inference: Because of the manipulation and control, experimental studies allow for stronger inferences about cause and effect. Observational studies, for example, can only show associations between variables; they can't definitively prove that one variable *causes* a change in another. While correlation doesn't equal causation, a well-designed experiment can demonstrate it.
* Replication: The controlled nature of experiments also makes them more easily replicable. Other researchers can repeat the study using the same methods and see if they obtain similar results. Replication is crucial for building confidence in the findings.
However, it's important to note that experimental studies aren't always feasible or ethical. Some research questions are impossible to study experimentally (e.g., the long-term effects of smoking), and ethical concerns may prevent researchers from manipulating certain variables. Despite this, when feasible and ethical, experimental studies remain the benchmark for establishing causal relationships, solidifying their "gold standard" status.