The myth of normality testing in biomedical research

Abstract

Testing data for normality before applying parametric statistics has become a routine procedure in biomedical research. This commentary argues that such tests provide little inferential value, may mislead analytical choices, and reflect outdated thinking from a pre-computational era. Parametric procedures are robust to modest departures from normality, and the Central Limit Theorem makes most normality checks unnecessary. According to this Theorem, the sampling distribution of the mean approaches a normal shape as sample size increases, regardless of the original distribution of the data. Outlier panic and indiscriminate data ranking further undermine the meaning of measurement and prediction. The obsession with distributional purity has replaced the logic of inference with statistical rigor. It is time to abandon this ritual and refocus on design, representativeness, and modeling – the true pillars of inference.