Ok so lets say I have multiple hypothesis that I want to test. Is there an advantage to testing them separately compared to all at once? Here is an example. Say theres a medication. We want to see how it affects males, how it affects females, and if the effect of the medication differs across gender. Say I record baseline health and health after the treatment for each individual. I could do a paired t test for the females. Then a paired t test for the males. Then I can do a two sample t test on the differences between males and females. Or I can do them all at once using simultaneous inference. Is there any draw backs for one of them vs the other?