CertNexus Certified Data Science Practitioner (CDSP) Practice Exam

The approach that focuses on identifying and controlling the variables in an experiment is known as Experimental Design. This methodology is essential in scientific research because it allows for the systematic investigation of causal relationships between variables. By controlling variables, researchers can isolate the effect of the independent variable on the dependent variable, which enhances the reliability and validity of the findings.

In Experimental Design, key elements such as randomization, replication, and control groups are utilized to minimize bias and account for confounding variables. This structured approach is crucial for determining whether observed changes in the dependent variable are indeed due to manipulations of the independent variable rather than extraneous factors.

While statistical analysis is concerned with interpreting data and drawing inferences, it does not primarily focus on how to manage variables during the research process. Regression analysis, although powerful for modeling relationships between variables, still requires a well-structured experimental design to yield meaningful results. Sampling techniques pertain to how subjects or data are selected for inclusion in a study and do not intrinsically involve the control of variables within the experimental framework.