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Sagot :
Permutation test is used to synthesize the population distribution of the statistic of interest for hypothesis testing of mean, median, and quartiles
An estimation of the population distribution—the distribution from which our observations originated—is the goal of a permutation test. We can then calculate how uncommon our observed values are in comparison to the population.
I find the permutation test to be more natural, and it only relies on the assumption of random assignment of treatment groups.
In a permutation test, you compare the observed test statistic to the distribution of values
you get when the treatment group assignments are shuffled/randomized/permuted.
The null hypothesis that two distinct groups originate from the same distribution is tested using permutation tests, also known as exact tests, randomization tests, or re-randomization tests. A permutation test can be used for hypothesis testing or significance testing (including A/B testing) without requiring any presumptions to be made about the distribution of the samples, such as that the samples must be normally distributed.
To know more about permutation here
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