Kolmogorov-Smirnov Test

A Kolmogorov-Smirnov Test is a nonparametric test used to determine if a sample comes from a population with a specific distribution.

• AKA: K-S Criterion.
• Context:
  • It can be used to determine whether two datasets differ significantly.
  • It can be used as a goodness-of-fit test.
  • …
• Counter-Example(s):
  • a Anderson–Darling Test.
• See: Hypothesis Test, Null Hypothesis, Normality test, Cramer-Von Mises Test, Shapiro–Wilk Test.

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References

2016

• (Wikipedia, 2016) ⇒ http://www.wikiwand.com/en/Kolmogorov%E2%80%93Smirnov_test Retrieved 2016-07-31
  • In statistics, the Kolmogorov–Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous, 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 Kolmogorov–Smirnov statistic quantifies a distance between the empirical distribution function of the sample and the cumulative distribution function of the reference distribution, or between the empirical distribution functions of two samples. The null distribution of this statistic is calculated under the null hypothesis that the sample is drawn from the reference distribution (in the one-sample case) or that the samples are drawn from the same distribution (in the two-sample case). In each case, the distributions considered under the null hypothesis are continuous distributions but are otherwise unrestricted.

The two-sample K–S test is one of the most useful and general nonparametric methods for comparing two samples, as it is sensitive to differences in both location and shape of the empirical cumulative distribution functions of the two samples.

The Kolmogorov–Smirnov test can be modified to serve as a goodness of fit test. In the special case of testing for normality of the distribution, samples are standardized and compared with a standard normal distribution. This is equivalent to setting the mean and variance of the reference distribution equal to the sample estimates, and it is known that using these to define the specific reference distribution changes the null distribution of the test statistic: see below.

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