Normaalsetest

Normaalsetest is a term used in some Dutch-language statistical writings to refer to a class of procedures for assessing whether a data set comes from a normal distribution. The purpose of such a test is to evaluate the normality assumption underlying many parametric methods, including t-tests, ANOVA, and regression diagnostics. A normaalsetest typically produces a p-value or a test statistic that quantifies the agreement between the observed data and a normal model with estimated parameters.

Approaches commonly associated with normaalsetest include formal normality tests such as the Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov tests, as well as approximate or composite methods that combine goodness-of-fit metrics with graphical checks like Q-Q plots. The exact implementation varies by software, and some formulations emphasize sensitivity to sample size or to the estimation of population parameters.

Applications span many fields, including psychology, finance, quality control, and social sciences, wherever assumptions of normality underpin statistical inference.

Limitations include that normality tests can be overly sensitive with large samples, while having limited power against certain alternatives with small samples. The practical takeaway is often to use a combination of graphical assessment and formal tests, and to apply robust methods or bootstrap procedures when normality is questionable.

See also: normal distribution; normality test; Shapiro–Wilk test; Kolmogorov–Smirnov test; Anderson–Darling test.