![]() It is named after Andrey Kolmogorov and Nikolai Smirnov. In essence, the test answers the question "How likely is it that we would see a collection of samples like this if they were drawn from that probability distribution?" or, in the second case, "How likely is it that we would see two sets of samples like this if they were drawn from the same (but unknown) probability distribution?". ![]() In statistics, the Kolmogorov–Smirnov test ( K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two samples (two-sample K–S test). The red line is a model CDF, the blue line is an empirical CDF, and the black arrow is the KS statistic. ![]() Non-parametric statistical test between two distributions Illustration of the Kolmogorov–Smirnov statistic.
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