I’m working on transitioning our undergrad introductory statistics course to one that involves simulation-based ideas alongside parametric, theory-based models. At the risk of sounding naïve,
I would like your opinion on the following: With the advent of computers that make simulation-based
p-value calculations fast and easy, and for those of you that teach this idea in parallel with theory-based
p-values, how do you explain the motivation for even using theory-based
p-values to your students?
In other words, parametric models require assumptions that may not be met. If we can use simulation to obtain a reliable
p-value without worrying about these assumptions, then why bother with a
t-test (e.g.) for a p-value at all? It seems like a historical argument (“It’s been done and probably will continue to be done for quite some time”) is one, but… I’m left feeling like I have to tell my students to conduct these theory-based tests
“just because.”
Thanks for your thoughts.
Megan