| > May I ask you why you chose to use the normal distribution in your example or any distribution at all, for that matter? The distribution is not important, any other data generator would do. > Which means that the null hypothesis is always true no matter what data you collect trying to reject it. The idea behind the thought experiment was that we live in a world in which researchers always investigate things that will turn out not to exist / be real, but the researchers themselves don't know this!, otherwise they wouldn't bother to run the investigations in the first place. > In fact, in your example, since you are essentially running 1000 hypothesis tests on different samples, multiple hypothesis correction would solve the "problem" with p-value. They're not multiple tests. They're multiple simulations of the same test, to show how the test performs in the long run. Perhaps you're a wonderful statistician, I wouldn't know, but nothing you have said thus far about null hypothesis significance testing makes any sense or is even remotely correct. |