# Kolmogorov–Smirnov test¶

The null hypothesis of the Kolmogorov–Smirnov test is that a dataset comes from a certain distribution; the reference distribution can be specified explicitely (one-sample test) or by an empirical sample (two-sample test). The alternative hypothesis is that the cumulative distributions of the sample is different (tail=:both; default), smaller (tail=:left), or larger (tail:=right) than the reference cumulative distribution. The exact test is based on the exact distribution of the differences whereas the approximate test is derived from its asymptotic distribution.

ExactOneSampleKSTest{T<:Real}(x::AbstractVector{T}, d::UnivariateDistribution)

Perform a one sample Kolmogorov–Smirnov-test of the null hypothesis that the data in vector x comes from the distribution d against the alternative hypothesis that the sample is not drawn from d.

Implements: pvalue

ApproximateOneSampleKSTest{T<:Real}(x::AbstractVector{T}, d::UnivariateDistribution)

Perform an asymptotic one sample Kolmogorov–Smirnov-test of the null hypothesis that the data in vector x comes from the distribution d against the alternative hypothesis that the sample is not drawn from d.

Implements: pvalue

ApproximateTwoSampleKSTest{T<:Real, S<:Real}(x::AbstractVector{T}, y::AbstractVector{S})

Perform an asymptotic two sample Kolmogorov–Smirnov-test of the null hypothesis that x and y` are drawn from the same distribution against the alternative hypothesis that the distribution comes from different distributions.

Implements: pvalue

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