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Archive of posts filed under the Teaching category.

Regression: What’s it all about? [Bayesian and otherwise]

Regression: What’s it all about? Regression plays three different roles in applied statistics: 1. A specification of the conditional expectation of y given x; 2. A generative model of the world; 3. A method for adjusting data to generalize from sample to population, or to perform causal inferences. We could also include prediction, but I […]

Define first, prove later

This post by John Cook features a quote form a book “Calculus on Manifolds,” by Michael Spivak which I think was the textbook for a course I took in college where we learned how to prove Stokes’s theorem, which is something in multivariable calculus involving the divergence and that thing that you get where you […]

New time unit needed!

We need a time unit that’s bigger than a minute but smaller than an hour. I thought of it when writing this comment in which I referred to “2100 valuable minutes of classroom time” during the semester (that’s 75 minutes per class, twice a week, for 14 weeks). A minute of class time is pretty […]

Stock-and-flow and other concepts that are important in statistical modeling but typically don’t get taught to statisticians

Bill Harris writes: You’ve written about causality somewhat often, and you, along with perhaps everyone who has done anything with statistics, have written that “correlation is not causation.” When you say that correlation is not causation, you seem to be pointing out cases where correlation exists but causality does not. While that’s important, there’s another […]

“A small but growing collection of studies suggest X” . . . huh?

Lee Beck writes: I’m curious if you have any thoughts on the statistical meaning of sentences like “a small but growing collection of studies suggest [X].” That exact wording comes from this piece in the New Yorker, but I think it’s the sort of expression you often see in science journalism (“small but mounting”, “small […]

“Academics should be made accountable for exaggerations in press releases about their own work”

Fernando Martel Garcia points me to this news article by Ben Goldacre: For anyone with medical training, mainstream media coverage of science can be an uncomfortable read. It is common to find correlational findings misrepresented as denoting causation, for example, or findings in animal studies confidently exaggerated to make claims about treatment for humans. But […]

Statistical Significance – Significant Problem?

John Carlin, who’s collaborated on some of my recent work on Type S and Type M errors, prepared this presentation for a clinical audience. It might be of interest to some of you. The ideas and some of the examples should be familiar to regular readers of this blog, but it could be useful to […]

“Peer assessment enhances student learning”

Dennis Sun, Naftali Harris, Guenther Walther, and Michael Baiocchi write: Peer assessment has received attention lately as a way of providing personalized feedback that scales to large classes. . . . By conducting a randomized controlled trial in an introductory statistics class, we provide evidence that peer assessment causes significant gains in student achievement. The […]

Discussion with Steven Pinker connecting cognitive psychology research to the difficulties of writing

Following up on my discussion of Steven Pinker’s writing advice, Pinker and I had an email exchange that cleared up some issues and raised some new ones. In particular, Pinker made a connection between the difficulty of writing and some research findings in cognitive psychology. I think this connection is really cool—I’ve been thinking and […]

Why I keep talking about “generalizing from sample to population”

Someone publishes some claim, some statistical comparison with “p less than .05″ attached to it. My response is: OK, you see this pattern in the sample. Do you think it holds in the population? Why do I ask this? Why don’t I ask the more standard question: Do you really think this result is statistically […]