Assessing statistical assumptions

Most statistical tests rely on assumptions about the data generating process. Violations of these assumptions can lead to biased estimates, incorrect confidence intervals, and misleading p values. Assessing assumptions is therefore a critical step before selecting and interpreting any statistical test.

Importantly, assumptions should not be treated as rigid rules. Their relevance depends on study design, sample size, and the intended inference. The goal is not perfection, but informed and transparent analysis.

Why assumptions matter

When assumptions are seriously violated, statistical results may no longer reflect the underlying research question. For example, non-normal data in small samples can invalidate parametric tests, while dependence between observations can severely underestimate uncertainty.

In applied research, especially clinical and observational studies, assumption checking should be viewed as a diagnostic process rather than a binary pass or fail exercise.