I'm confused as to when adjustments for multiple tests need to be used. I'm an undergrad who's familiarising himself with stats, so apologies if my question sounds dumb, but I haven't found any satisfactory answer myself. I understand the purpose of these corrections as well as that there are different approaches (controlling FWER vs. FDR). That being said, I have found many people not using any such corrections except for specific cases such as multiple comparisons in ANOVA. One of our lecturers said that you're adjusting when the tests are not independent. Others have told me that it doesn't really matter and as long as you're following NHST, you should control for all your tests performed on one sample (seems kinda strict). Some say that you should decide based on what your "family of tests" is. And also, others seem to highlight the importance of formulating the hypotheses a priori, in which case you don't always need to adjust. Is there some link between all of these views that I am missing?
Also, suppose I'm using ANOVA with 2 categorical predictors and an interaction. Why don't I need to adjust my F-test p-values for each of the predictors and the interaction? Also, when we use multiple comparisons or contrasts, why do we adjust taking into account only these tests and not including the p-values from the F-tests?