Comparing distributions: The Two-Sample Anderson-Darling Test as an Alternative to the Kolmogorov-Smirnoff Test

摘要

This paper introduces the two-sample Anderson-Darling (AD) test of goodness of fit as a tool for comparing distributions, response time distributions in particular. We discuss the problematic use of pooling response times across participants, and alternative tests of distributions, the most common being the Kolmogorov-Smirnoff (KS) test. We compare the KS test and the AD test, presenting conclusive evidence that the AD test is more powerful: when comparing two distributions that vary (1) in shift only, (2) in scale only, (3) in symmetry only, or (4) that have the same mean and standard deviation but differ on the tail ends only, the AD test proves to detect differences better than the KS test. In addition, the AD test has a type I error rate corresponding to alpha whereas the KS test is overly conservative. Finally, the AD test requires less data than the KS test to reach sufficient statistical power.