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Statistical validity evaluates the correctness of the statistical treatments applied to experimental data and the overall soundness of the researchers' numerical conclusions. It depends on selecting the appropriate inferential tests (such as $$t$$-tests or ANOVA) based on the study's design and the measurement scales of the dependent variable, as well as ensuring that the underlying mathematical assumptions of those tests are met. A common threat to statistical validity is an inadequate sample size, which limits the ability of the statistical tests to reliably support the conclusions drawn from the data.

Statistical Validity

A power analysis is a statistical calculation performed during the design phase of a study to determine the exact number of participants needed to reliably detect a specific expected effect. Because the likelihood of finding a statistically significant relationship between variables depends heavily on both the size of the sample and the number of experimental conditions, researchers conduct this analysis to ensure their study has sufficient statistical power to test their hypothesis effectively.

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