What measures can researchers take to reduce confounding variables?

Choosing to use control groups and random assignment is an effective strategy for researchers to reduce confounding variables. Control groups help establish a baseline by allowing researchers to compare results against a group that does not receive the experimental treatment. This comparison can clarify whether the treatment itself is causing the observed effects, rather than confounding factors.

Random assignment plays a critical role in this process as well, as it ensures that participants are allocated to different groups in a way that is independent of their characteristics. This helps to evenly distribute potential confounding variables across all groups, minimizing their influence on the outcomes of the study. In essence, by utilizing control groups and random assignment, researchers can more confidently attribute any changes in behavior or outcomes to the independent variable they are studying, rather than to extraneous factors.

Increasing the sample size alone may improve the generalizability or statistical power of the study but does not directly address the issue of confounding variables. Focusing solely on qualitative measures limits the types of analyses that can be performed and does not inherently control for confounding effects. Finally, while the idea of eliminating all variables sounds ideal, it is practically impossible in research, as some extraneous factors will always exist. Hence, employing control groups and random assignment provides a structured and effective