I'm not an expert on the field, but I need to know more about how to combined multiple significance test p-values after a two sample Kolmogorov Smirnov (KS)test on two distributions.
But I have two cases: 1) a model where I can generate as many realizations I wish and 2) only one (or a few) observed realization.
1) I run multiple KS tests, each test gives me a p-value: I used the Fisher Method and everything goes as expected in literature Fisher tesi in CrossValidate and Fisher test in CrossValidated(unifiorm distribution of p-values if hypothesis cannot be reject).
Now, since I could complicate the model in different ways, I was wondering if I can also use the Bonferroni-Holm correction Wiki-page on method for the same dataset with multiple p-values (meta-analysis).
And which is actually the difference between Bonferrroni-type corrections and Fisher-type ones (or Stouffer type), more in terms when to use them, which are the requirements and conditions and limitations. For example multiple comparison is indented for different tests with different hypothesis, or inter-dependence among tests
2) what changes about the methods, if I have only 1 observed sequence and I use Bootstrapping o permutation test to artificially created several sequences and making again K-S stests and several p-values again.