Teaching Statistics: Great Talks and Resources Now Online

This conference (as in pic below) was planned as a gathering of, maybe, 30 folks. But then it had to go online and the organisers (Kevin Peters and Fegal O’Hagan, of Trent University, and Rob Cribbie, of York University) found themselves scrambling to expand their Zoom limit, as more than 600 folks from around the world registered. Success!

You can see here the list of speakers, the abstracts, and video recordings of most of the talks, and links to slides and other resources provided by many of the speakers.

Our talk

Teaching the New Statistics, Now With Better Software

Bob’s and my talk was the last—at traditional conferences the grave-yard slot when many have already fled to the airport. It was at 6am in the winter dark for me, but that was OK. At the site, scroll to the end to see our links, then click the down arrow to see our abstract.

My job was to demonstrate the great new software: esci web by Gordon Moore, and esci in jamovi by Bob. Both are freely accessible from the esci menu at our site. For esci web, search at our site for ‘Gordon’ to find four blog posts.

Some starting points in the video of our talk:

    10.47 esci web

    25.35 dance of the p values, in esci web

    35.30 variability of p values with replication: Significance Roulette

    40.00 esci in jamovi

        41.40 two independent groups example

        45.50 single group example

        47.41 meta-analysis

        50.02 the diamond ratio (heterogeneity in a meta-analysis)

        50.07 interaction in a two-way design

The conference day ran from my 11 pm to 7 am, so I managed to catch only a few other talks. These included:

Andy Field

Teaching statistics: Damnation and deliverance

Lively and engaging, this talk included a dazzling array of videos in various punk and gothic styles (I may have those terms a bit wrong…) designed to highlight statistical ideas. Definitely different, even if, I suspect, not to everyone’s taste. However, given the wild success of his statistics textbooks, Andy must be doing many things right.

Christopher Green

The Replication Crisis: What should we teach to undergraduates, and when?

Interesting discussion of Open Science—why it’s needed and what we should teach about it. Starting at 21.55 are some arguments for not teaching undergraduates about meta-analysis. I’m not persuaded, while fully agreeing that selective publication (thank you NHST) is an enormous problem for meta-analysis—and science.

E.J. (Eric-Jan Wagenmakers)

Tips and tricks for teaching Bayesian statistics

E.J. was in good form, as lively and compelling as ever. I took my first course on Bayesian statistics in 1965 and have read books and attended many workshops since. The logic, and the match with how human cognition works have always appealed. I’ve kept looking out for simple and practical ways to introduce Bayesian methods in the intro statistics course for psychology students. Ideally, these should also help seasoned researchers brought up in the NHST tradition (ugh!) to understand and adopt Bayesian methods. I’m still looking, which is why I’ve focused on traditional frequentist CIs as the practical way forward, at least at first. I’d be more than happy to see Bayesian estimation, based on credible intervals, and Bayesian modelling much more widely used.

At 5.30 see E.J.’s book—a free downloadBayesian thinking for toddlers, which presents an ingenious and extremely simple way to introduce the core Bayesian idea of using evidence to update belief.

From 20.42 E.J. is talking about JASP, the wonderful SPSS-killing open-source statistics application that his team has been developing for some time. It supports frequentist as well as Bayesian methods. Bob is working with the team towards have esci available within JASP before too long.

At 45.40 E.J. recommends Bayesian Statistics the Fun Way by Will Kurt. I’ve sent off for it—perhaps this will give me the easy way in that I’ve long been seeking?

Themed Session: The Flipped Classroom

Bob and I have reports that some instructors are successfully using ITNS and its online resources for a flipped-classroom approach. That’s been great to hear—flipping may be becoming widespread, especially after 2020, and we hope that ITNS2 and its materials will be even more flip-friendly. So I was especially interested to see these three talks from flipsters at McMaster and York Universities.

Flipping inferential statistics

The flipped classroom improves performance in introductory statistics: Early evidence from a systematic review and meta-analysis

This is the one talk of the three for which a video is available, at least at present. The meta-analysis included 11 studies comparing flipped with lecture formats. The overall mean effect size advantage for flipped was g = 0.40, with part of that attributable to use of regular quizzes.

No tutorials, no problem: The inspiration, planning, and execution of flipping a Statistics II course

Wesley Burr

Teaching Reproducibility and Replicability in Statistics

This talk was just before mine; I caught the last part. Great enthusiasm and engagement. Lots on Open Science. What seemed like excellent advice on using R from the start, via R Markdown. Pointers to resources.


I highly recommend browsing the abstracts and at least dipping in to the videos. Please make a comment below on any you find especially interesting or useful. Thanks.


P.S. Congratulations and thanks to Kevin, Fegal, and Rob for their initiative and vast amount of work. There are already discussions about when another such conference might be organized.

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