APS in San Fran 2: Symposium on Teaching the New Stats

Our symposium was titled Open Science and Its Statistics: What We Need to Teach Now. The room wasn’t large, but it was packed, standing room only. I thought the energy was terrific. There were four presentations, as below.

Bob and Tamarah Smith have set up an OSF page on Getting started teaching the New Statistics. It holds all sorts of goodies, including the slides for our symposium.

At that site, expand OSF Storage, then Open Science and its Statistics–2018 APS Symposium slides and see 4 files for the 4 presentations:

Bob Calin-Jageman (Chair)
Open Science and Its Statistics: What We Need to Teach Now
Examples of students being stumped by traditional NHST analysis and presentation of a result, but readily (and happily) understanding the same result presented using the new stats. In addition we should teach and advocate the new statistics to improve statistical understanding and communication across all of science that uses statistical inference.

Geoff Cumming
Open Science is Best Practice Science
Being ethical as a teacher or researcher requires that we use current best practice, and for statistical inference that is the new statistics. The forest plot is, in my experience, a highly effective picture for introducing students to estimation and meta-analysis. I gave a paper advocating its use in the intro stats course back in 2006. In my experience, the new stats is, in contrast to NHST, a joy to teach. Students saying ‘It just all makes sense…’ is one of the most heart-warming things any teacher can hear.

Susan Nolan & Kelly Goedert
Transitioning a Traditional Department: Roadblocks and Opportunities for Incorporating the New Statistics and Open Science into Teaching Materials
Roadblocks include NHST appearing everywhere, common software (SPSS) not making the new stats easy, colleagues who are steeped in the old ways, widely-used textbooks taking traditional approaches, … But there are new textbooks and open source software emerging, and there are strategies for spreading the word and bringing colleagues on board. (See the slides for numerous practical suggestions and links to many useful resources to support teaching and using the new statistics.)

Tamarah Smith
Feeling Good about the New Statistics: How the New Statistics Improves the Way Researchers and Students Feel about Statistics
Statistics anxiety is a problem for many students, and impedes their learning. The new statistics opens up great opportunities for teaching so that anxiety is much reduced and students’ attitudes are more positive. The new statistics helps teachers meet the GAISE guidelines for assessment and instruction in stats education. Students feel better and more engaged and their learning is grounded. As a result their teachers also feel better. Let’s do it.

Personally, I found it wonderful to hear so many examples and reasons all converging on the conclusion that teaching the new statistics (1) is what’s needed for ethical science, (2) helps students understand much better and feel good about their learning, and (3) is great for teachers also. A triple win!


P.S. The pic below is from Bob’s slides and is adapted from Kruschke and Liddell (2018). The crucial thing for Open Science is the shift to estimation and meta-analysis, and away from the damaging dichotomous decision making of NHST. The estimation and meta-analysis can be frequentist (conventional confidence intervals) or Bayesian (credible intervals)–either is fine. In other words, there is a Bayesian new statistics, alongside the new statistics of ITNS. Maybe the Bayesian version will come to be the more widely used? I believe the biggest hurdle to overcome for that to happen is the arrival of good teaching materials that make Bayesian estimation easily accessible to beginning students, as well as to researchers steeped in NHST.

But the main message is that either of the cells in the bottom row is just what we need.

Kruschke, J. K. & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25, 178–206.

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