Shapiro-Wilk normality test — when useful?

medium

Answer

$Tests whether data comes from a normal distribution.$
$Very sensitive for n < 50; over-powered for large n (reports significant non-normality for trivial deviations).$
$Practical rule: for large samples, rely more on Q-Q plots than on the p-value.$
$Alternatives: Anderson-Darling (weighted toward tails), Kolmogorov-Smirnov (less sensitive), Lilliefors (KS variant with estimated params).$

Check yourself — multiple choice

Always trust p-value
Tests normality; sensitive at small n / over-powered at large n → complement with Q-Q plots at scale
Only for chi-square
Not a test

Shapiro-Wilk: normality test; complement with Q-Q at large n.

#hypothesis-testing

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