Comparing sample distributions with the Kolmogorov-Smirnov (KS) test

Often in statistics we need to understand if a given sample comes from a specific distribution, most commonly the Normal (or Gaussian) distribution. For this intent we have the so-called normality tests, such as Shapiro-Wilk, Anderson-Darling or the Kolmogorov-Smirnov test. All of them measure how likely a sample is to have come from a normal distribution, with a related p-value to support this measurement. The Kolmogorov-Smirnov test, however, goes one step further and allows us to compare two samples, and tells us the chance they both come from the same distribution. This test is really useful for evaluating regression and classification models, as will be explained ahead. <a href="https://towardsdatascience.com/comparing-sample-distributions-with-the-kolmogorov-smirnov-ks-test-a2292ad6fee5">Click Here</a>