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Archive of posts filed under the Miscellaneous Statistics category.

The Use of Sampling Weights in Bayesian Hierarchical Models for Small Area Estimation

All this discussion of plagiarism is leaving a bad taste in my mouth (or, I guess I should say, a bad feeling in my fingers, given that I’m expressing all this on the keyboard) so I wanted to close off the workweek with something more interesting. I happened to come across the above-titled paper by […]

Message to Booleans: It’s an additive world, we just live in it

Boolean models (“it’s either A or (B and C)”) seem to be the natural way that we think, but additive models (“10 points if you have A, 3 points if you have B, 2 points if you have C”) seem to describe reality better—at least, the aspects of reality that I study in my research. […]

Damn, I was off by a factor of 2!

I hate when that happens. Demography is tricky. Oh well, as they say in astronomy, who cares, it was less than an order of magnitude!

Don’t, don’t, don’t, don’t . . . We’re brothers of the same mind, unblind

Hype can be irritating but sometimes it’s necessary to get people’s attention (as in the example pictured above). So I think it’s important to keep these two things separate: (a) reactions (positive or negative) to the hype, and (b) attitudes about the subject of the hype. Overall, I like the idea of “data science” and […]

The Fallacy of Placing Confidence in Confidence Intervals

Richard Morey writes: On the tail of our previous paper about confidence intervals, showing that researchers tend to misunderstand the inferences one can draw from CIs, we [Morey, Rink Hoekstra, Jeffrey Rouder, Michael Lee, and EJ Wagenmakers] have another paper that we have just submitted which talks about the theory underlying inference by CIs. Our […]

Subtleties with measurement-error models for the evaluation of wacky claims

Paul Pudaite writes: In the latest Journal of the American Statistical Association (September 2014, Vol. 109 No. 507), Andrew Harvey and Alessandra Luati published a paper [preprint here] — “Filtering With Heavy Tails” — featuring the phenomenon you had asked about (“…(non-Gaussian) models for which, as y gets larger, E(x|y) can actually go back toward […]

Quantitative literacy is tough! Or, I had no idea that, in 1958, 96% of Americans disapproved of interracial marriage!

Mark Palko linked to this data-rich cartoon by Randall Munroe: And I was stunned, first by the data on interracial marriage and then, retrospectively, by my earlier ignorance of these data. Was approval of interracial marriage only 4% in 1958? I had no idea. I looked it up at the Gallup site and it seems […]

The hype cycle starts again

Completely uncritical press coverage of a speculative analysis. But, hey, it was published in the prestigious Proceedings of the National Academy of Sciences (PPNAS)! What could possibly go wrong? Here’s what Erik Larsen writes: In a paper published in the Proceedings of the National Academy of Sciences, People search for meaning when they approach a […]

This is what “power = .06” looks like. Get used to it.

I prepared the above image for this talk. The calculations come from the second column of page 6 of this article, and the psychology study that we’re referring to is discussed here.

“Differences Between Econometrics and Statistics” (my talk this Monday at the University of Pennsylvania econ dept)

Differences Between Econometrics and Statistics:  that’s the title of the talk I’ll be giving at the econometrics workshop at noon on Monday. At 4pm 4:30pm in the same place, I’ll be speaking on Stan. And here are some things for people to read: For “Differences between econometrics and statistics”: Everyone’s trading bias for variance at […]