I am performing multiple t-tests to compare conditions of data. I have had to resort to permutation based t-tests (based on difference of means) due to high variance between samples.
My question is if I am using permutation based t-tests is FWE correction still required?
My understanding is that because is it non-parametric the probability of a false negative is not contingent on the number of tests applied to the same set of data. So FWE correction should not be required?
I will expand on my specific issue.
I am comparing scan-path data for subjects looking at two different types of faces (inverted and upright). I need to compare a number of measures for different face areas (eyes, nose , mouth, ears and 'other') between the areas. For this I have set up a t-test for each area, for each measure, and then correct for multiple comparisons. For example, I am testing the number of fixations, and fixation duration for each face area. I am constrained these tests in order to be able to compare to previous work. I have not been able to rely on any omnibus test thus far because of the non normality of my data greatly reduced the power of any omnibus test I have tried.