Permutation test of the null hypothesis that a set of data was sampled from a symmetric distribution with a particular mean. The test is based on a t-statistic and can be applied to situations in which a one sample or paired sample/repeated measures t-test is appropriate. Note, that this test is more general than parametric t-tests in that it does not assume that the data were sampled from a Gaussian distribution.
This function can perform the test on one variable or simultaneously on multiple variables. When applying the test to multiple variables, the "tmax" method is used for adjusting the p-values of each variable for multiple comparisons (Blair & Karniski, 1993). Like Bonferroni correction, this method adjusts p-values in a way that controls the family-wise error rate. However, the permutation method will be more powerful than Bonferroni correction when different variables in the test are correlated.
Blair, R.C. & Karniski, W. (1993) An alternative method for significance testing of waveform difference potentials. Psychophysiology.
David Groppe (2021). mult_comp_perm_t1(data,n_perm,tail,alpha_level,mu,reports,seed_state) (https://www.mathworks.com/matlabcentral/fileexchange/29782-mult_comp_perm_t1-data-n_perm-tail-alpha_level-mu-reports-seed_state), MATLAB Central File Exchange. Retrieved .
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