Dear Megan,

My simple answer is the one you gave. That is, the course I teach is almost completely a service course. I actually teach at an institution where the only intro stat class is the one that I (as the statistician on campus) control (I can't teach all sections - there are too many - but I pick the book, design the syllabus, etc.)

I switched our course to teaching both simulation and traditional methods together about two years ago, after having many talks with the client disciplines about the idea that students should come out of such a course having a better understanding of the ideas of inference, and yet still know the traditional methods that these departments are still using. Unfortunately one department has gotten so upset with our teaching these "new-fangled" ideas that they have seriously discussed dropping the major requirement for our course and teaching their own intro stat class. Trust me when I say that this would be a disaster in all kinds of ways for their students.

So I'm walking a very tight line. I tell my students that I think it is very important for them to see both kinds of methods. I tell them that I'm convinced that in about 10 years, all or most client disciplines will be using simulation based methods. So the students should be exposed to them now. But, I go on, there are many people still out there using the traditional methods, and there are many research papers previously published (and continuing to be published) that use the traditional methods. For that reason, the students still need to be exposed to the traditional methods.

Will this work in the long run? I don't know. This is the first time in over 30 years that this particular department has complained loudly about the intro stat course in the math/stat department.

Does that help?

I suppose it really depends on who your audience is and how progressive the client departments are at your institution.

Ann Cannon

Professor of Statistics

Cornell College

Mt. Vernon, Iowa

On Fri, Jan 2, 2015 at 1:13 PM, Olson Hunt, Megan <olsonhum@uwgb.edu> wrote:

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

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