Teaching statistics

Alex Reinhart – Updated May 25, 2018 notebooks · refsmmat.com

See also Statistical misconceptions, Pedagogy more generally, and Statistical programming languages for thoughts on programming in early statistics classes.

We usually teach statistics in the same way it was taught to us: with lectures, backed by a tedious textbook and perhaps some labs using antiquated statistical software. We joke that students usually hate introductory statistics courses, but it is no joke, as evidenced by the people who routinely react as though I’ve admitted to an interesting sexually transmitted disease when I say I study statistics. (The reactions tend to be worse than when I was a physics major, surprisingly enough.)

Unfortunately the literature on methods to teach statistics is fairly thin.

High level overview

Assessment

The first question is: How well do we teach statistics?

See also Student assessment.

For statistics, it seems like a worthwhile starting place would be trawling the literature for common misconceptions and errors expressed by students in intro classes, then explicitly designing questions to elicit these. See Statistical misconceptions.

Interesting case studies and curricula

Active learning

I’m a fan of peer instruction (see Pedagogy), but there have been only a few evaluations of similar active learning methods in statistics:

Developing expertise

Students find it difficult to develop expert thinking, particularly in tasks like selecting the right analysis or the right graphics to use for a problem. They may be able to interpret results, but the meta-skill of picking the right analysis to interpret is only taught implicitly.

Randomization-based intro courses

I am easily swayed by the claim that teaching probability theory, distributions, t tests and their requisite assumptions, chi-squared tests, etc. is all wasted effort on nonmathematical students who need an introductory statistics course for basic statistical literacy. We need months of background material (probability, distributions, the central limit theorem) before we can get to any interesting statistical ideas, like inference.

Randomization-based courses, where we just bootstrap and permute everything, are appealing because a simple two-sample permutation test can be explained on the second day of class, without weeks of background. We can get right into the logic of inference and sampling variation without the overhead.

There’s a group at Hope College that has developed a textbook and a series of papers on the effectiveness of the method: