Anderson Darling Normality Test Python. In its basic form the test. An Anderson-Darling Test is a goodness of fit test that measures how well your data fit a specified distribution.
This test is most commonly used to determine whether or not your data follow a normal distribution. This can provide the basis for a more thorough interpretation of the result. It was developed in 1952 by Theodore Anderson and Donald Darling.
The Anderson Darling tests whether the sample from the population follows a particular distribution.
It takes as parameters the data sample and the name of the distribution to test it against. It is a modification of the Kolmogorov-Smirnov K-S testand gives more weight to the tails than does the K-S test. Feb 18 2021 The Anderson-Darling test tests the null hypothesis that a sample is drawn from a population that follows a particular distribution. Is there a way to compute p value from the given ouputs.
