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Regression to the mean acts as a severe threat to internal validity when participants are selected for a research study specifically because of their extreme initial scores. Statistically, individuals who achieve exceptionally high or low scores on a pretest are highly likely to score closer to the population average on a subsequent posttest simply due to natural, random variability. This makes it incredibly difficult to determine whether a shift in scores was genuinely caused by the experimental treatment or was merely the result of statistical regression.

Regression to the Mean as a Threat to Internal Validity

To effectively rule out threats to internal validity—such as history, maturation, testing, instrumentation, regression to the mean, and spontaneous remission—researchers can add a control group to a pretest-posttest design. A control group does not receive the experimental treatment but is subjected to the same external events, passage of time, and repeated measurements as the treatment group. By comparing the posttest changes between the two groups, researchers can isolate the actual effect of the treatment from these alternative explanations, although doing so means the study is no longer a one-group design.

Control Group in Pretest-Posttest Designs

An everyday example of regression to the mean involves an amateur bowler who typically averages a score of $$150$$. If she suddenly bowls an exceptionally high game of $$220$$, statistical probability suggests her next game will be significantly lower, regressing back toward her long-term average of $$150$$. This natural fluctuation demonstrates how extreme performances or scores are usually followed by more typical, average results, rather than indicating a permanent improvement or decline.

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