# Anderson–Darling test¶

The null hypothesis of the Anderson–Darling test is that a dataset comes from a certain distribution; the reference distribution can be specified explicitely (one-sample test). K-sample Anderson–Darling tests are available for testing whether several samples are coming from a single population drawn from the distribution function which does not have to be specified.

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

Perform a one sample Anderson–Darling 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

KSampleADTest{T<:Real}(xs::AbstractVector{T}...; modified=true)

Perform an k-sample Anderson–Darling test of the null hypothesis that the data in vectors xs comes from the same distribution against the alternative hypothesis that the samples comes from different distributions.

modified paramater enables a modified test calculation for samples whose observations do not all coincide.

Implements: pvalue

References:

• k-Sample Anderson-Darling Tests, F. W. Scholz and M. A. Stephens, Journal of the American Statistical Association, Vol. 82, No. 399. (Sep., 1987), pp. 918-924.