Ann Cannon, Cornell College
I teach in a very small department (we just increased from 3.5 to 4.5 tenure track positions this year), but the support for statistics at Cornell College is pretty amazing. Consider, for instance that for at least 30 years, one of those tenure track positions in the math department has been held by a statistician (me for the last 22 years). I’m also proud of the fact that for 40+ years the college has had a single introductory statistics course with multiple sections. This course is required for several majors and is the prerequisite for courses across the curriculum. Finally, when I was hired, the department and I agreed that when I taught math, I’d teach it the way the mathematicians wanted me to teach it, and when they taught stat, they’d teach it the way I wanted them to teach stat. This agreement continues today, though I rarely teach math anymore.
…in workshop fashion, I helped my math colleagues to explore the new material.
Erin Blankenship, University of Nebraska-Lincoln
Like many statistics faculty who completed their graduate training during the last century, my preparation for teaching went something like this: I was handed a book (Moore & McCabe, 2nd edition—it’s still on my shelf). While TA training–at least at my institution–has evolved since then, it had to adapt further to prepare TAs for the simulation-based inference approach.
… [TAs] also attended the large class meetings and so could see how I was implementing the simulation methods.
R Pruim, Calvin College
I’m not writing to convince you that you should use R to teach simulation-based inference (SBI). My goal is to convince you that you can use R for SBI, even with students (and instructors) who have never used R before. Along the way I’ll mention some guiding principles and illustrate some tools that my colleagues Danny Kaplan and Nick Horton and I have assembled in the
mosaic R package to make SBI (and EDA and traditional inference procedures) much easier.
The biggest key to using R well is to provide a lot of creative opportunity with as little R as possible.