Teachers of introductory statistics are increasingly using simulation-based methods to help students learn concepts and methods of statistical inference. Two quick anecdotes:

• The theme of the 2011 U.S. Conference on Teaching Statistics was “The Next BIG Thing,” and the consensus emerging from the conference was that the BIG thing is teaching introductory statistics with simulation-based methods.

• The recently conducted International Conference on Teaching Statistics in Flagstaff (July 2014) included a large number of sessions on this topic, featuring presenters from around the world, often with standing-room-only crowds of attendees. Continue reading

# Monthly Archives: October 2014

# How did I get started on teaching simulation-based inference?

**Robin Lock, St Lawrence University**

Around 1998, Allan Rossman and Beth Chance asked me to help out with a new edition of their popular Workshop Statistics book that would be adapted to use a new software package called Fathom that was being developed by Bill Finzer, then at KCP Technologies. [pullquote]But I could detect light bulbs going on with students thinking, “Oh, that’s what he means by seeing what would happen if the null hypothesis is true!'[/pullquote] Fathom has a lot of neat tools designed to allow students to explore statistical concepts, including a facility to allow students to easily select a sample from a dataset, define any statistic for that sample, and then quickly generate a new dataset with values of that statistic for many new samples. Continue reading

# Focusing on the statistical process

**Jill Vanderstoep – Hope College**

To be completely honest, I didn’t choose to teach with simulation-based methods. Nathan Tintle made a curricular change, and I followed. To this day, I am so glad I did because the changes have been the most refreshing and fun changes I have made in my Statistics classes over the twenty plus years I have been teaching.

[pullquote]I am so glad I did because the changes have been the most refreshing and fun changes I have made in my Statistics classes over the twenty plus years I have been teaching.[/pullquote] Continue reading

# Development of the CATALST course

**The CATALST group – University of Minnesota**

**JOAN GARFIELD**

Inspired by George Cobb’s plenary address at the first USCOTS in 2005, we began to explore ways to turn his ideas into an actual curriculum. We decided to explore the use of models and modeling in the course, and, funded by a NSF grant, developed the CATALST curriculum. [pullquote] Our guiding principle was to teach students to really cook, rather than follow recipes.[/pullquote] Our goal was to develop a course that focused on randomization methods and random sampling, taking away the traditional focus on the two-sample *t*-test. The CATALST course went through many iterations and had input from a team of great collaborators, including courageous graduate students who taught early versions of this radically different course. Our guiding principle was to teach students to really cook, rather than follow recipes. The cooking method uses randomization and repeated sampling methods to make statistical inferences. Even though there were many challenges, we feel that we developed a course that engages students and stimulates them to think, build and test models, and understand the core ideas of statistical inference. Continue reading

# Why I chose to teach with simulation-based methods

**Josh Tabor, Canyon del Oro High School**

*Short answer*: I teach with simulation-based methods because I believe they make it easier for students to understand the logic of inference and see statistics as a complete investigative process from asking questions to drawing conclusions.

[pullquote]

I settled on two guiding principles that would inform the way I designed the course:

- Emphasize that Statistics is an investigative process, not a set of isolated skills.
- Stay focused on the logic of inference.

[/pullquote]

# Why did I choose to teach with simulation based methods?

**Chris Malone, Winona State University**

I started teaching at Winona State University in 2002. I am fortunate to be able to teach a variety of statistics courses to students with an even wider variety of backgrounds here at Winona State. About 10 years ago, at the advice of a senior faculty member, I started doing statistical consulting work to balance my teaching duties. This had a remarkable impact on my teaching at that time. Consulting continues to this day to shape my approach to teaching statistics.

[pullquote]The traditional sequence was too compartmentalized and did not allow much time for students to conduct a statistical analysis from start-to-finish.[/pullquote] Continue reading

# My rationale to choose teach statistics with simulation-based methods

**Dave Klanderman** , **Trinity Christian College**

It would be accurate to say that I was a skeptic when I showed up at a week-long MAA workshop at Dordt College in June 2013. At the urging of my departmental colleague, who is now serving as our Provost, I signed up to learn more about a new statistics textbook, a new paradigm for teaching and learning statistics, and a chance to connect with both friends and family in Sioux Center, Iowa.[pullquote]Additional sessions convinced me that this approach had merit and the comparison data using the CAOS provided the final piece of assessment evidence.[/pullquote]

# How I saw the light

**Lacey Echols, Butler University**

I knew I was in trouble teaching statistics when I always thought I was doing such a great job, but the students were totally lost the last four weeks of the semester! It didn’t matter how many times I used active learning in my classroom or the how many great lectures I thought I presented, they just did not get the concept of a hypothesis test. [pullquote] it is wonderful to present the ideas of how to think about research questions, the proper way to write hypotheses, the meaning of a p-value, and the meaning of statistically significant within the first four days of a semester.[/pullquote] The early part of the semester with data description, probability, and experimental design had lulled them into a false sense of security.