This has probably been asked before, but I've been thinking about it a bit today and I wanted to see what you folks thought. A lot of introductory statistics courses usually set up one-sided z- and t-tests the following way: H0: mu = mu_0, H1: mu > mu_0 (or < mu_0) I recently finished a mathematical statistics course, and I can't really say that I like this setup, mostly because it leaves out a huge chunk of the parameter space. I understand that, functionally, changing the null hypothesis to be an inclusive inequality (ex. mu <= mu_0) doesn't really change anything about the test since alpha is still defined at the point of equality. Is there a reason to prefer one setup over the other, or is it purely a personal preference? submitted by /u/kyaputenorima [link] [comments]