Contents
Do permutation tests assume normality?
An important assumption behind a permutation test is that the observations are exchangeable under the null hypothesis. An important consequence of this assumption is that tests of difference in location (like a permutation t-test) require equal variance under the normality assumption.
What is the purpose of the permutation test?
A permutation test5 is used to determine the statistical significance of a model by computing a test statistic on the dataset and then for many random permutations of that data. If the model is significant, the original test statistic value should lie at one of the tails of the null hypothesis distribution.
What are the assumptions of the significance test?
The common assumptions made when doing a t-test include those regarding the scale of measurement, random sampling, normality of data distribution, adequacy of sample size, and equality of variance in standard deviation.
What can a permutation test be used for?
A permutation test gives a simple way to compute the sampling distribution for any test statistic, under the strong null hypothesis that a set of genetic variants has absolutely no e ect on the
Do you have to assume Gaussian distribution for permutation test?
In fact, a classical parametric two-sample test (with equal variance) makes not just the same assumption, but also further assumes that patients and controls come from the same Gaussian distribution. Permutation tests do not require Gaussianity; it suffices that the data are merely exchangeable.
Is the observed statistic a random sample from the permutation distribution?
The observed test statistic can be considered a random sample from the permutation distribution because it is equally likely to have arisen from any case-control re-labeling given the null hypothesis.
Can a null hypothesis be violated in a permutation test?
However, even exchangeability can be violated in the presence of dependence among observations, and it may not always be clear what to permute. The aim of this blog post is to emphasize the relevance of linking the null hypothesis and the dependence structure within the data to what should be shuffled in a permutation test.