Kolmogorov–Smirnov test

The empirical distribution function F_n for n independent and identically distributed (i.i.d.) ordered observations X_i is defined as

${\displaystyle F_{n}(x)={1 \over n}\sum _{i=1}^{n}I_{[-\infty ,x]}(X_{i})}$

where ${\displaystyle I_{[-\infty ,x]}(X_{i})}$ is the indicator function, equal to 1 if ${\displaystyle X_{i}\leq x}$ and equal to 0 otherwise.

The Kolmogorov–Smirnov statistic for a given cumulative distribution function F(x) is

${\displaystyle D_{n}=\sup _{x}|F_{n}(x)-F(x)|}$

where sup_x is the supremum of the set of distances. By the Glivenko–Cantelli theorem, if the sample comes from distribution F(x), then D_n converges to 0 almost surely in the limit when ${\displaystyle n}$ goes to infinity. Kolmogorov strengthened this result, by effectively providing the rate of this convergence (see Kolmogorov distribution). Donsker's theorem provides a yet stronger result.

In practice, the statistic requires a relatively large number of data points (in comparison to other goodness of fit criteria such as the Anderson–Darling test statistic) to properly reject the null hypothesis.

Illustration of the Kolmogorov distribution's PDF.

The Kolmogorov distribution is the distribution of the random variable

${\displaystyle K=\sup _{t\in [0,1]}|B(t)|}$

where B(t) is the Brownian bridge. The cumulative distribution function of K is given by[2]

${\displaystyle \operatorname {Pr} (K\leq x)=1-2\sum _{k=1}^{\infty }(-1)^{k-1}e^{-2k^{2}x^{2}}={\frac {\sqrt {2\pi }}{x}}\sum _{k=1}^{\infty }e^{-(2k-1)^{2}\pi ^{2}/(8x^{2})},}$

which can also be expressed by the Jacobi theta function ${\displaystyle \vartheta _{01}(z=0;\tau =2ix^{2}/\pi )}$. Both the form of the Kolmogorov–Smirnov test statistic and its asymptotic distribution under the null hypothesis were published by Andrey Kolmogorov,[3] while a table of the distribution was published by Nikolai Smirnov.[4] Recurrence relations for the distribution of the test statistic in finite samples are available.[3]

Under null hypothesis that the sample comes from the hypothesized distribution F(x),

${\displaystyle {\sqrt {n}}D_{n}{\xrightarrow {n\to \infty }}\sup _{t}|B(F(t))|}$

in distribution, where B(t) is the Brownian bridge.

If F is continuous then under the null hypothesis ${\displaystyle {\sqrt {n}}D_{n}}$ converges to the Kolmogorov distribution, which does not depend on F. This result may also be known as the Kolmogorov theorem. The accuracy of this limit as an approximation to the exact cdf of ${\displaystyle K}$ when ${\displaystyle n}$ is finite is not very impressive: even when ${\displaystyle n=1000}$, the corresponding maximum error is about ${\displaystyle 0.9\%}$; this error increases to ${\displaystyle 2.6\%}$ when ${\displaystyle n=100}$ and to a totally unacceptable ${\displaystyle 7\%}$ when ${\displaystyle n=10}$. However, a very simple expedient of replacing ${\displaystyle x}$ by

${\displaystyle x+{\frac {1}{6{\sqrt {n}}}}+{\frac {x-1}{4n}}}$

in the argument of the Jacobi theta function reduces these errors to ${\displaystyle 0.003\%}$, ${\displaystyle 0.027\%}$, and ${\displaystyle 0.27\%}$ respectively; such accuracy would be usually considered more than adequate for all practical applications.[5]

The goodness-of-fit test or the Kolmogorov–Smirnov test can be constructed by using the critical values of the Kolmogorov distribution. This test is asymptotically valid when ${\displaystyle n\to \infty }$. It rejects the null hypothesis at level ${\displaystyle \alpha }$ if

${\displaystyle {\sqrt {n}}D_{n}>K_{\alpha },\,}$

where K_α is found from

${\displaystyle \operatorname {Pr} (K\leq K_{\alpha })=1-\alpha .\,}$

The asymptotic power of this test is 1.

Fast and accurate algorithms to compute the cdf ${\displaystyle \operatorname {Pr} (D_{n}\leq x)}$ or its complement for arbitrary ${\displaystyle n}$ and ${\displaystyle x}$, are available from:

• [6] and [7] for continuous null distributions with code in C and Java to be found in.[6]

• [8] for purely discrete, mixed or continuous null distribution implemented in the KSgeneral package [9] of the R project for statistical computing, which for a given sample also computes the KS test statistic and its p-value. Alternative C++ implementation is available from.[8]

Illustration of the two-sample Kolmogorov–Smirnov statistic. Red and blue lines each correspond to an empirical distribution function, and the black arrow is the two-sample KS statistic.

The Kolmogorov–Smirnov test may also be used to test whether two underlying one-dimensional probability distributions differ. In this case, the Kolmogorov–Smirnov statistic is

${\displaystyle D_{n,m}=\sup _{x}|F_{1,n}(x)-F_{2,m}(x)|,}$

where ${\displaystyle F_{1,n}}$ and ${\displaystyle F_{2,m}}$ are the empirical distribution functions of the first and the second sample respectively, and ${\displaystyle \sup }$ is the supremum function.

For large samples, the null hypothesis is rejected at level ${\displaystyle \alpha }$ if

${\displaystyle D_{n,m}>c(\alpha ){\sqrt {\frac {n+m}{n\cdot m}}}.}$

Where ${\displaystyle n}$ and ${\displaystyle m}$ are the sizes of first and second sample respectively. The value of ${\displaystyle c({\alpha })}$ is given in the table below for the most common levels of ${\displaystyle \alpha }$

${\displaystyle \alpha }$	0.20	0.15	0.10	0.05	0.025	0.01	0.005	0.001
${\displaystyle c({\alpha })}$	1.073	1.138	1.224	1.358	1.48	1.628	1.731	1.949

and in general[18] by

${\displaystyle c\left(\alpha \right)={\sqrt {-\ln \left({\tfrac {\alpha }{2}}\right)\cdot {\tfrac {1}{2}}}},}$

so that the condition reads

${\displaystyle D_{n,m}>{\sqrt {-\ln \left({\tfrac {\alpha }{2}}\right)\cdot {\tfrac {1+{\tfrac {m}{n}}}{2m}}}}.}$

Here, again, the larger the sample sizes, the more sensitive the minimal bound: For a given ratio of sample sizes (e.g. ${\displaystyle m=n}$), the minimal bound scales in the size of either of the samples according to its inverse square root.

Note that the two-sample test checks whether the two data samples come from the same distribution. This does not specify what that common distribution is (e.g. whether it's normal or not normal). Again, tables of critical values have been published. A shortcoming of the Kolmogorov–Smirnov test is that it is not very powerful because it is devised to be sensitive against all possible types of differences between two distribution functions. Some argue[19][20] that the Cucconi test, originally proposed for simultaneously comparing location and scale, can be much more powerful than the Kolmogorov–Smirnov test when comparing two distribution functions.

While the Kolmogorov–Smirnov test is usually used to test whether a given F(x) is the underlying probability distribution of F_n(x), the procedure may be inverted to give confidence limits on F(x) itself. If one chooses a critical value of the test statistic D_α such that P(D_n > D_α) = α, then a band of width ±D_α around F_n(x) will entirely contain F(x) with probability 1 − α.

A distribution-free multivariate Kolmogorov–Smirnov goodness of fit test has been proposed by Justel, Peña and Zamar (1997).[21] The test uses a statistic which is built using Rosenblatt's transformation, and an algorithm is developed to compute it in the bivariate case. An approximate test that can be easily computed in any dimension is also presented.

The Kolmogorov–Smirnov test statistic needs to be modified if a similar test is to be applied to multivariate data. This is not straightforward because the maximum difference between two joint cumulative distribution functions is not generally the same as the maximum difference of any of the complementary distribution functions. Thus the maximum difference will differ depending on which of ${\displaystyle \Pr(x<X\land y<Y)}$ or ${\displaystyle \Pr(X<x\land Y>y)}$ or any of the other two possible arrangements is used. One might require that the result of the test used should not depend on which choice is made.

One approach to generalizing the Kolmogorov–Smirnov statistic to higher dimensions which meets the above concern is to compare the cdfs of the two samples with all possible orderings, and take the largest of the set of resulting K–S statistics. In d dimensions, there are 2^d−1 such orderings. One such variation is due to Peacock[22] (see also Gosset[23] for a 3D version) and another to Fasano and Franceschini[24] (see Lopes et al. for a comparison and computational details).[25] Critical values for the test statistic can be obtained by simulations, but depend on the dependence structure in the joint distribution.

In one dimension, the Kolmogorov–Smirnov statistic is identical to the so-called star discrepancy D, so another native KS extension to higher dimensions would be simply to use D also for higher dimensions. Unfortunately, the star discrepancy is hard to calculate in high dimensions.

The Kolmogorov-Smirnov test (one or two sampled test verifies the equality of distributions) is implemented in many software programs:

• Mathematica has KolmogorovSmirnovTest
• MATLAB has kstest in its Statistics Toolbox.
• The R package "KSgeneral"[9] computes the KS test statistics and its p-values under arbitrary, possibly discrete, mixed or continuous null distribution.
• R's statistics base-package implements the test as ks.test {stats} in its "stats" package.
• SAS implements the test in its PROC NPAR1WAY procedure.
• Python has an implementation of this test provided by SciPy[26] by Statistical functions (scipy.stats)
• SYSTAT (SPSS Inc., Chicago, IL)
• Java has an implementation of this test provided by Apache Commons[27]
• KNIME has a node implementing this test based on the above Java implementation[28]
• StatsDirect (StatsDirect Ltd, Manchester, UK) implements all common variants.
• Stata (Stata Corporation, College Station, TX) implements the test in ksmirnov (Kolmogorov–Smirnov equality-of-distributions test) command. [29]
• PSPP implements the test in its KOLMOGOROV-SMIRNOV (or using K-S shortcut function.
• The Real Statistics Resource Pack for Excel runs the test as KSCRIT and KSPROB [30]

• Lepage test
• Cucconi test
• Kuiper's test
• Shapiro–Wilk test
• Anderson–Darling test
• Cramér–von Mises test

• Daniel, Wayne W. (1990). "Kolmogorov–Smirnov one-sample test". Applied Nonparametric Statistics (2nd ed.). Boston: PWS-Kent. pp. 319–330. ISBN 978-0-534-91976-4.
• Eadie, W.T.; D. Drijard; F.E. James; M. Roos; B. Sadoulet (1971). Statistical Methods in Experimental Physics. Amsterdam: North-Holland. pp. 269–271. ISBN 978-0-444-10117-4.
• Stuart, Alan; Ord, Keith; Arnold, Steven [F.] (1999). Classical Inference and the Linear Model. Kendall's Advanced Theory of Statistics. 2A (Sixth ed.). London: Arnold. pp. 25.37–25.43. ISBN 978-0-340-66230-4. MR 1687411.
• Corder, G. W.; Foreman, D. I. (2014). Nonparametric Statistics: A Step-by-Step Approach. Wiley. ISBN 978-1118840313.
• Stephens, M. A. (1979). "Test of fit for the logistic distribution based on the empirical distribution function". Biometrika. 66 (3): 591–595. doi:10.1093/biomet/66.3.591.

• "Kolmogorov–Smirnov test", Encyclopedia of Mathematics, EMS Press, 2001 [1994]
• Short introduction
• KS test explanation
• JavaScript implementation of one- and two-sided tests
• Online calculator with the KS test
• Open-source C++ code to compute the Kolmogorov distribution and perform the KS test
• Paper on Evaluating Kolmogorov’s Distribution; contains C implementation. This is the method used in Matlab.
• Paper on Computing the Two-Sided Kolmogorov–Smirnov Distribution; computing the cdf of the KS statistic in C or Java.
• Paper powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions; Jeff Alstott, Ed Bullmore, Dietmar Plenz. Among others, it also performs the Kolmogorov–Smirnov test. Source code and installers of powerlaw package are available at PyPi.