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  "body": "\n## Summary\n\nThe Anderson–Darling test is a goodness-of-fit [[hypothesis test|087P03NEND]] for whether a sample comes from a given distribution.\nIts statistic is an integrated squared difference between the [[empirical distribution function|A05WR6TEK5]] and the hypothesised distribution function, weighted so that the tails count more.\nIt is a weighted form of the [[Cramér–von Mises criterion|G14XJXPKFS]], and it is often more sensitive than the [[Kolmogorov–Smirnov test|YZYHWC6C5F]] to departures in the tails.\n\n## Explanations\n\n### L1 human\n\nNear the far ends of a distribution the staircase of the data and the expected curve are both close to $0$ or to $1$.\nSo a gap there looks small, even if it means that the data have too many or too few extreme values.\nThe Anderson–Darling test divides each squared gap by $F_0(1 - F_0)$, which is small in the tails, so that tail gaps count fully.\nThis makes it good at detecting heavy or light tails.\n",
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