[SBI] Explaining motivation for theory-based models

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