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Archive of posts filed under the Miscellaneous Statistics category.

“The harm done by tests of significance” (article from 1994 in the journal, “Accident Analysis and Prevention”)

Ezra Hauer writes: In your January 2013 Commentary (Epidemiology) you say that “…misunderstanding persists even in high-stakes settings.” Attached is an older paper illustrating some such. “It is like trying to sink a battleship by firing lead shot at it for a long time”—well put!

“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 […]

“Unbiasedness”: You keep using that word. I do not think it means what you think it means. [My talk tomorrow in the Princeton economics department]

The talk is tomorrow, Tues 24 Feb, 2:40-4:00pm in 200 Fisher Hall: “Unbiasedness”: You keep using that word. I do not think it means what you think it means. Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University Minimizing bias is the traditional first goal of econometrics. In many cases, though, the […]

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 […]

Another example of why centering predictors can be good idea

Andrew Dolman writes: Just in case you need another example of why it is important to consider what the intercepts in a model represent, here is a short comment I [Dolman] just got published correcting a misinterpretation of a simple linear model, that would not have happened if they had centered their predictor around a […]

Two Unrecognized Hall Of Fame Statisticians

To follow up on a recent post, I thought it would be amusing to consider the most important unrecognized statisticians. That is, those statisticians of the past who made important contributions which have been largely forgotten. Any suggestions? Dead people only, please.

“When Do Stories Work? Evidence and Illustration in the Social Sciences”: My talk in the Harvard sociology dept this Thurs noon

Stories are central to social science. It might be pleasant to consider stories as mere adornments and explications of theories that we develop and evaluate via formal data collection, but it seems that all of us—including statisticians!—rely on stories to develop our understanding of the social world. And therein lies a paradox: stories are valued […]

Statistical analysis recapitulates the development of statistical methods

There’s a old saying in biology that the development of the organism recapitulates the development of the species: thus in utero each of us starts as a single-celled creature and then develops into an embryo that successively looks like a simple organism, then like a fish, an amphibian, etc., until we reach our human form […]

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 […]

Six quick tips to improve your regression modeling

It’s Appendix A of ARM: A.1. Fit many models Think of a series of models, starting with the too-simple and continuing through to the hopelessly messy. Generally it’s a good idea to start simple. Or start complex if you’d like, but prepare to quickly drop things out and move to the simpler model to help […]