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The Bell Curve

I spent too much of one day last week reading this article and everything it links to. Charles Murray, one of the authors of The Bell Curve, also has a piece in the August 2005 issue of Statistical Science called “How to Accuse the Other Guy of Lying with Statistics” (part of a special section […]

President’s Invited Address

Rod Little gave the President’s Invited Address at the Joint Statistical Meetings in Minneapolis earlier this month. He was talking about the Bayesian/frequentist “schism” and resolved it in the following way: Bayesian methods are good for inference; frequentist methods are good for model assessment. I like that. (I’m not ashamed of being interested in the […]

Overheard in Harvard Square

Fall 2003, while the Boston Red Sox and Chicago Cubs were both still in the playoffs. Girl on cell phone: But if the Red Sox and Cubs both go to the World Series, that means one of them will have to win. But that’s a probability zero event, so that would, like, unmake existence.

Bad Graphs

One of the links on this blog is to Junk Charts, which shows and discusses all kinds of good and bad graphics found in various news sources. It reminded me of one bad graph that was printed in Amstat News of all places, showing that statisticians (or at least statistics-related publications) aren’t immune to graphical […]

Terrorist Risk Revisited

There’s a fun little article in the Harvard Magazine on risk perception. David Ropeik and George Gray at the Harvard School of Public Health wrote a book Risk: A Practical Guide for Deciding What’s Really Safe and What’s Really Dangerous in the World around You, which sounds interesting. The article also mentions a study by […]

20-minute wait on the GW…

How does the traffic reporter on the radio know how long the wait to get across a bridge or through a tunnel is? Do people collect data on this? Is the reported wait time merely a function of how long the “line” leading to said bridge or tunnel is? Or are other factors (maybe time […]

Statistical Crystal Ball

Dean Foster, Lyle Ungar, and Choong Tze Chua at the University of Pennsylvania have created a mortality calculator. It’s pretty cool–you enter all kinds of information about your health, habits, family history, etc., and it predicts how long you’ll live. Not to brag, but my predicted life span is 94 years, with upper quartile 103.99.

More on Software Validation

Andrew and I have both written here about our Software Validation paper with Don Rubin. The last thing to add on the topic is that my website now has newly updated software to implement our validation method (go down to Research Software and there are .zip and .tar versions of the R package). If the […]

Teaching Example

There was a fun little article in the New York Times a while back (unfortunately I can’t find it now and am missing some of the numbers, but the main idea still holds) about income differences across New York City’s five boroughs. Apparently the mean income in the Bronx is higher than in Brooklyn, even […]

Lying with Statistics

I hope I’m not just contributing to the gossip mill, but the latest post on the Freakonomics blog is kind scary.