USCOTS 2019 - Program

The ASA GAISE Report for school outlines the practice of statistics (or statistical problem solving!) as appropriate at the school level. It also gives examples. But what actually happens in the classroom? This talk will introduce the Big Ideas underlying the practice of statistics at school and give some examples of Australian research in classrooms and the associated outcomes. In the classroom we want students to think like statisticians but of course they do not have the tools that statisticians have for evaluating the evidence arising from the data they collect.

The speakers will discuss what they learned from editing (with Nicole Lazar) the 43 articles in the special issue ofThe American Statistician on “Statistical Inference in the 21st Century: A World Beyond P < 0.05.” After briefly reviewing the “Don’ts” set forth in the ASA Statement on P-Values and Statistical Significance—and adding a new one—they offer their distillation of the wisdom of the many voices in the special issue (and the broader literature): specific Do’s for teaching, doing research, and informing decisions as well as underlying principles for good statistical practice.

We invite and strongly encourage all of you to attend the USCOTS banquet, always a conference highlight, on Friday evening. In addition to offering a delicious meal and opportunities for enlightening conversations, Rob Gould from UCLA will present Citizen Statisticians Then and Now: The Last 8 Years.

Frequentist and Bayesian statistical methods typically are taught in separate courses. I propose instead that frequentist and Bayesian methods can be fruitfully taught together. By setting the two approaches side by side, the goals and concepts of each approach are delineated more clearly. The teaching framework incorporates the essential topics of (i) hypothesis testing and (ii) estimation with uncertainty. The progression of topics is guided with a two-by-two table (based on the framework of Kruschke & Liddell, 2018).

There is a lot a statistician can (and should) consider when evaluating evidence, and not all of it can be mastered after a single course. What aspects of evaluating evidence are most important for our introductory students to understand? I’ll provide my answer here, drawing from backgrounds in causal inference and statistics education, and will open this up for more general discussion in the breakout to follow.