Confounding variables are extraneous variables that can influence the relationship between an independent and dependent variable in a study, leading to inaccurate conclusions about the relationship being studied. Confounding variables affect both the independent and dependent variables, and they can influence the dependent variable directly and either correlate with or causally affect the independent variable. Confounding variables can be related to both the supposed cause and the supposed effect of the study, and they can be difficult to separate from the true effect of the independent variable.

Examples of confounding variables in psychology include inherent aptitude, previous knowledge, age, height, education level, rural or urban upbringing, country, order effects, participant variability, social desirability effect, Hawthorne effect, demand characteristics, and evaluation apprehension. Confounding variables can produce spurious or distorted associations between two variables, and they can exaggerate the causal effect of the independent and dependent variables because the association is inflated by the effect of the confounding variable on both variables.

To reduce the impact of confounding variables, researchers can use several methods, including restriction, matching, randomization, and statistical control. Restriction involves restricting the treatment group by only including subjects with the same values of potential confounding factors. Matching involves matching subjects on the confounding variable before assigning them to different groups. Randomization involves randomly assigning subjects to different groups to ensure that confounding variables are equally distributed across groups. Statistical control involves including the confounding variable as a covariate in the statistical analysis.

In summary, confounding variables are extraneous variables that can influence the relationship between an independent and dependent variable in a study, leading to inaccurate conclusions about the relationship being studied. They can be related to both the supposed cause and the supposed effect of the study, and they can be difficult to separate from the true effect of the independent variable. Researchers can use several methods to reduce the impact of confounding variables, including restriction, matching, randomization, and statistical control.