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Concept
Permutation test
Summary
A permutation test (also called re-randomization 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.
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.
To illustrate the basic idea of a permutation test, suppose we collect random variables and for each individual from two groups and whose sample means are and , and that we want to know whether and come from the same distribution. Let and be the sample size collected from each group. The permutation test is designed to determine whether the observed difference between the sample means is large enough to reject, at some significance level, the null hypothesis H that the data drawn from is from the same distribution as the data drawn from .
The test proceeds as follows. First, the difference in means between the two samples is calculated: this is the observed value of the test statistic, .
Next, the observations of groups and are pooled, and the difference in sample means is calculated and recorded for every possible way of dividing the pooled values into two groups of size and (i.
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