A permutation test (also called re-randomization test or shuffle test) is an exact statistical hypothesis test making use of the proof by contradiction.
A permutation test involves two or more samples. The null hypothesis is that all samples come from the same distribution . Under the null hypothesis, the distribution of the test statistic is obtained by calculating all possible values of the test statistic under possible rearrangements of the observed data. Permutation tests are, therefore, a form of resampling.
Permutation tests can be understood as surrogate data testing where the surrogate data under the null hypothesis are obtained through permutations of the original data.[1]
In other words, the method by which treatments are allocated to subjects in an experimental design is mirrored in the analysis of that design. If the labels are exchangeable under the null hypothesis, then the resulting tests yield exact significance levels; see also exchangeability. Confidence intervals can then be derived from the tests. The theory has evolved from the works of Ronald Fisher and E. J. G. Pitman in the 1930s.
Permutation tests should not be confused with randomized tests.[2]
^Moore, Jason H. "Bootstrapping, permutation testing and the method of surrogate data." Physics in Medicine & Biology 44.6 (1999): L11.
^Onghena, Patrick (2017-10-30), Berger, Vance W. (ed.), "Randomization Tests or Permutation Tests? A Historical and Terminological Clarification", Randomization, Masking, and Allocation Concealment (1 ed.), Boca Raton, FL: Chapman and Hall/CRC, pp. 209–228, doi:10.1201/9781315305110-14, ISBN 978-1-315-30511-0, retrieved 2021-10-08
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