Independent sample permutation test based one of three t-statistics (equal variance, Welch's t, t_dif). 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 & Karnisky, 1993; Westfall & Young, 1993). Like Bonferroni correction, this method adjusts p-values in a way that strongly 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.
David Groppe (2020). mult_comp_perm_t2(data1,data2,n_perm,tail,alpha_level,mu,t_stat,reports,seed_state) (https://www.mathworks.com/matlabcentral/fileexchange/54585-mult_comp_perm_t2-data1-data2-n_perm-tail-alpha_level-mu-t_stat-reports-seed_state), MATLAB Central File Exchange. Retrieved .
Welch's t added as a possible test statistic. This is somewhat robust to differences in variance between the groups being tested and is almost as powerful as the standard pooled variance t-statistic when variances are equal between groups.
RandStream now called correctly for most recent versions of MATLAB when seed state is needed.