COVID has all of us doing things that we’re not used to doing—like not leaving the house for two weeks in a row, or holding meetings in your daughter’s bedroom. In this way, it’s encouraging all of us to innovate. In my case, this means a new PhD-level course, Introduction to Probability and Statistics, and a new way of teaching through a flipped classroom model.

I’ve never taught stats before, and I’ve never taught using an asynchronous flipped classroom model, so this will be new all around. But I’ve profited from lots of discussion of how to teach statistics, how to make flipped classroom experiences work, and from thinking about my own experience taking Introduction to Statistics with Tasos Kalandrakis back in fall 2001, another unusual time.* I’ve especially learned from Gary King’s GOV 2001, which has been teaching some of the same material to the same sorts of students using a similar flipped classroom format for some time now.** It is entirely possible that a flipped classroom model is the best way to teach introductory probability and statistics.

For those curious, here is the syllabus (PDF).

I also feel it necessary to acknowledge all of the materials that were instrumental for helping me to prepare.

• Wikipedia
• Wonnacott and Wonnacott’s Introductory Statistics, my own textbook
• Angrist and Pischke’s Mastering ‘Metrics
• Aronow and Miller’s Foundations of Agnostic Statistics
• online notes from Sergio Garcia-Rios, Jeremy Orloff and Jonathan Bloom, Maya Sen, and Brandon Stewart

Acknowledging these online notes, part of me says “just go take these courses instead” but maybe my own remix will be fun too. It does include slides such as this, for example.

But more seriously, what makes my course special, I think, are three things:

• Assuming only basic mathematical background: nothing beyond basic algebra. This is serious: anyone who can do junior high algebra can complete this course.
• Teaching R and Stata at the same time: no pen and paper homework, all problem sets done exclusively via the computer. (I also assume no computer science, scripting, or programming background.)
• An emphasis on developing intuitions via simulation, as a complement to analytical results (which cannot involve anything more advanced than basic algebra, of course).

The objective here is fundamentals for everyone. We sacrifice some more advanced concepts and results in favor of intuitions and understanding the basics at what I like to call the “no-bullshit” level. If this works out how I hope, then students with no background or particular inclination towards probability or statistics will be able to understand how this stuff works, to consume the quantitative social science research that they encounter, and will be ready for those more advanced courses out there.***

NOTES

* As is typical, I did not appreciate at the time just how good that class was, and how hard it must have been to teach.
** You can also watch all of his lectures here.
*** They may also appreciate some dad jokes.