Thank you both very much for your responses, Ann and Beth. It's reassuring. I think the transition argument is a good one. "This is where we're going, but this is where we've been and that approach will still be around for quite some time." I too am the only statistics faculty (and in my first year, at that) and it feels like a lot of pressure to make the "right" decision about course content, so it's helpful to hear others' takes on issues. Thanks again, Megan Megan J. Olson Hunt, PhD Assistant Professor, Statistics University of Wisconsin-Green Bay -----Original Message----- From: sbi-bounces(a)causeweb.org [mailto:sbi-bounces@causeweb.org] On Behalf Of sbi-request(a)causeweb.org Sent: Friday, January 02, 2015 1:27 PM To: sbi(a)causeweb.org Subject: SBI Digest, Vol 2, Issue 5 Send SBI mailing list submissions to sbi(a)causeweb.org To subscribe or unsubscribe via the World Wide Web, visit https://www.causeweb.org/mailman/listinfo/sbi or, via email, send a message with subject or body 'help' to sbi-request(a)causeweb.org You can reach the person managing the list at sbi-owner(a)causeweb.org When replying, please edit your Subject line so it is more specific than "Re: Contents of SBI digest..." Today's Topics: 1. Explaining motivation for theory-based models (Olson Hunt, Megan) 2. Re: Explaining motivation for theory-based models (Ann Cannon) 3. Re: Explaining motivation for theory-based models (Beth Chance) ---------------------------------------------------------------------- Message: 1 Date: Fri, 2 Jan 2015 19:13:05 +0000 From: "Olson Hunt, Megan" <olsonhum(a)uwgb.edu> To: "'sbi(a)causeweb.org'" <sbi(a)causeweb.org> Subject: [SBI] Explaining motivation for theory-based models Message-ID: <8CBC5C8F1ACF1C4692AB0C048EA4F07724561D09(a)MAILBXB.uwgb.edu> Content-Type: text/plain; charset="iso-8859-1" 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 na?ve, 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