“Don’t ask” teaching in Statistics

This short Nature World view article by Jerry Ravetz is definitely worth a read by all teachers of Science. The title “Stop the Science Training That Demands ‘Don’t Ask'” pretty much says it all. He doesn’t explicitly mention teaching of Statistics — so I’m throwing this out there directly. Teaching of Statistics happens behind the scenes within classrooms, research, and mentoring — as well as in formal classes with statistical-sounding words in the title. It certainly flows through other scientific disciplines and media reporting of results (like the vacillating advice on nutrition Ravetz mentions).

In my experience related to the teaching Statistics within academia (both formally and informally), I think we need to add another layer to the appeal to stop “don’t ask” training. We often assume that those doing the training (again, formal or informal) have a sufficient depth of knowledge on the subject to have some control over perpetuating a “don’t ask” culture. But, such a culture can be conveyed unknowingly when the teacher does not possess the depth of knowledge or practical experience needed to open up space for asking deep questions and to promote meaningful discussions around them. And the most dangerous case is that when the teacher does not realize they don’t have that depth of knowledge. They may think they are offering an open culture of questioning, but the depth of the questions asked and answered is so superficial that it doesn’t get at what I believe Jerry Ravetz is arguing for in this article.

We must face, as a scientific community (not just Statistics community), that many of our teachers of Statistics cannot be expected to elicit or navigate the tough questions that need asking — and may not even realize there is much to ask. This is not the fault of the teachers themselves — it is the fault of a system of norms that has developed over decades within scientific culture. It is completely accepted by most for a math teacher, a researcher with training in another discipline, etc. to teach statistical inference, even formally in a classroom. This is not done out of blatant disregard for quality or ethics, but because it appears to have worked in the past (as judged by those who were also taught by such teachers) and we simply don’t have the workforce of people with depth of knowledge in statistical inference to meet the demand.

Imagine coming straight out of an undergraduate degree in Mathematics, showing up as a new graduate student in Statistics, and being handed your own course to teach statistical inference. You may be another university student’s first and only teacher of Statistics before they head out into the real world. You might laugh this off as unrealistic, but I assure you it is not. The mathematics involved in introductory statistics is low enough level that anyone with some math skills should be able to teach it, right? Never mind about the “inference” part of the story, hopefully they’ll pick that up somewhere else in life — both student and teacher.

For my first master of science degree (pre-Statistics life), I took a calculation-focused first course in statistics (literally using handheld calculators). I repeatedly asked about the difference between a standard deviation and a standard error and finally got an answer that may have been technically correct according to formulas, but was not satisfying conceptually. I would later have an “ah-hah” moment of understanding as a graduate student of Statistics when I was taught by people with PhDs in Statistics and years of experience working in science. I would also later realize how very little knowledge I had about statistical inference from my first master’s degree (though I did use statistical inference for my thesis!) and some doctoral work before then. At the time, I had no clue about how much I didn’t know, but proceeded to help my fellow doctoral students because I had (or at least thought I had) more knowledge and skills than they did. Had you asked me at the time, I probably would have thought I was qualified to teach an introductory class in Statistics. After a year in a Statistics graduate program, I would have changed my answer. After seven years and a PhD I would have added strong emphasis to the ‘no.’ I am now very thankful I did enter a program that immediately called on me to do something I was not prepared to do. And, I was not an undergraduate math student! I had a master’s degree of science and had done my own research using statistical inference!

It’s very uncomfortable to face the extent of the problem, largely because I don’t believe there is an easy solution to it. The only realistic option is probably more continuing education to get people to the point where they realize there is so much more to learn. The problem with this idea is that the people we really need to reach don’t think they need continuing education — the very source of the problem is hindering the fix of the problem. And there needs to be a workforce of teachers for the teachers — that doesn’t exist even close to the extent needed. Unfortunately, the current love affair with “data science” is likely to just make the problem worse — there are so many more attractive ways to spend resources than educating those who already have jobs that involve teaching. I apologize for ended on a pessimistic note, but I believe it is a realistic note.

Here is a paragraph from Ravetz’s article that should leave us with food for thought relative to the teaching and practice of statistical inference in Science today.

The philosopher Thomas Kuhn once compared taught science to orthodox theology. A narrow, rigid education does not prepare anyone for the complexities of scientific research, applications and policy. If we discourage students from inquiring into the real nature of scientific truths, or exploring how society shapes the questions that researchers ask, how can we prepare them to maintain public trust in science in our ‘post-truth’ world? Diversity and doubt produce creativity; we must make room for them, and stop funnelling future scientists into narrow specialties that value technique over thought.

Jerry Ravetz, Nov 19 2019 online version of Nature https://www.nature.com/articles/d41586-019-03527-y