Anderson-Darling Test

The Anderson-Darling test measures how well a set of data fits a particular distribution, giving extra weight to the tails, where a poor fit often matters most. A lower test value means a closer match.

Where it appears

When you fit a distribution to your own data in the Expression Builder, ProcessModel uses goodness-of-fit tests like Anderson-Darling to rank the candidate distributions and choose the best match, so you do not have to judge the fit by eye.

LegacyHow this worked in the previous version

The Anderson-Darling test is used to test if a sample of data came from a population with a specific distribution. It is a modification of the Kolmogorov-Smirnov (K-S) test and gives more weight to the tails than does the K-S test. The K-S test is distribution free in the sense that the critical values do not depend on the specific distribution being tested. The Anderson-Darling test makes use of the specific distribution in calculating critical values. This has the advantage of allowing a more sensitive test and the disadvantage that critical values must be calculated for each distribution.