Hi, I'm trying to decipher some code ...
if not self.fix_boundary:
weights = np.zeros(self.data.shape)
for i,d in enumerate(self.data):
Some of them have low power because the tests are unconditioned. So you would say, perform all the tests, filter according to the BH procedure, then throw out tests that turn out not to be sufficiently powerful at the end of the pipeline?
Suppose I am investigating a question which involves e.g., many statistical T-tests. The normal Benjamini-Hochberg procedures tells me how to control the false discovery rate. However, suppose that some or many of these tests do not have sufficient statistical power i.e., it falls below 0.8...