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.[pullquote]The biggest key to using *R* well is to provide a lot of creative opportunity with as little *R* as possible.[/pullquote]

# Category Archives: 04. How do I utilize technology?

# How Technology Transformed My Intro Stats Class

**Valorie Zonnefeld, Dordt College**

Have you ever thought about the reaction to the first slide rule in the 1620s? Do you think some people pulled up their noses, questioning whether students would forget their “calculations”? Did others dive in whole-heartedly, touting the advantages of teaching with their fancy new “slipsticks”? Regardless of the initial reaction, the slide rule has left its mark on the story line of mathematics. There is no question that technological developments continue to change how mathematics and statistics are taught. [pullquote]The ability to use technology to simulate random phenonema has made statistics more approachable to many students. [/pullquote]

# Different tools for different audiences

**The Catalyst Group, University of Minnesota**

** Matt Beckman, Ethan Brown, Bob delMas, Elizabeth Brondos Fry, Nicola Justice, Anelise Sabbag**

The Catalyst group at the University of Minnesota has developed two introductory statistics courses that employ simulation-based inference methods: an undergraduate course (EPSY 3264) and a graduate course (EPSY 5261). [pullquote]Simulation-based inference has different audiences, even in the introductory course[/pullquote] Continue reading

# Students building their own simulations: How hard can it be?

**Tim Erickson, Mills College**

Joan Garfield tells us that approaching inference using simulation is like teaching students to cook rather than simply to follow recipes. I’m totally on board with that. In this post, I want to reflect about students can also grow the vegetables—that is, become farmers as well as cooks—and build the simulations themselves. [pullquote]Yet I claim that making students responsible for the hard part is good for learning.[/pullquote] Continue reading

#
Moving from *learning statistics* to *discovering statistics*

**Scott Rifkin, UCSD**

I have tried several different approaches to using technology to help students get a better intuitive understanding of statistical concepts. Although statistical software has been used in introductory statistics classes for quite some time, interfaces that facilitate discovery-based learning rather than calculation are much newer. [pullquote]or I could make an applet specifically targeted towards this common question that will let her discover the answer for herself[/pullquote] Continue reading

# Reflections on my changing technology

**Beth Chance, Cal Poly – San Luis Obispo**

My technology use has definitely evolved, including in ways that surprise me! In particular, I have switched from being a long-time advocate of Minitab to not using a standard statistical package in the course at all. In reflecting on why this was the case, I realized I had some guiding principles: [pullquote]I want student to see simulation-based methods as equally legitimate to theory-based procedures, if not preferred in some situations. [/pullquote]