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Archive of posts filed under the Miscellaneous Statistics category.

“The great advantage of the model-based over the ad hoc approach, it seems to me, is that at any given time we know what we are doing.”

The quote is from George Box, 1979. And this: Please can Data Analysts get themselves together again and become whole Statisticians before it is too late? Before they, their employers, and their clients forget the other equally important parts of the job statisticians should be doing, such as designing investigations and building models? I actually […]

DataKind Opportunity Analyst Job Opening

Jake Porway writes: DataKind is looking for a brilliant part-time Opportunity Analyst to find data-informed solutions to the world’s most pressing problems with our NYC team! We’re a fast growing non-profit that tackles humanity’s biggest problems through data science. . . . We’ve helped the World Bank estimate poverty from satellite imagery, teamed with the […]

As if we needed another example of lying with statistics and not issuing a correction: bike-share injuries

This post is by Phil Price A Washington Post article says “In the first study of its kind, researchers from Washington State University and elsewhere found  a 14 percent greater risk of head injuries to cyclists associated with cities that have bike share programs. In fact, when they compared raw head injury data for cyclists […]

Average predictive comparisons in R: David Chudzicki writes a package!

Here it is: An R Package for Understanding Arbitrary Complex Models As complex models become widely used, it’s more important than ever to have ways of understanding them. Even when a model is built primarily for prediction (rather than primarily as an aid to understanding), we still need to know what it’s telling us. For […]

Hurricanes/himmicanes extra: Again with the problematic nature of the scientific publication process

Jeremy Freese has the story. To me, the sad thing is not that people who don’t understand statistics are doing research. After all, statistics is hard, and to require statistical understanding of all quantitative researchers would be impossible to enforce in any case. Indeed, if anything, one of the goals of the statistical profession is […]

My answer: Write a little program to simulate it

Brendon Greeff writes: I was searching for an online math blog and found your email address. I have a question relating to the draw for a sports tournament. If there are 20 teams in a tournament divided into 4 groups, and those teams are selected based on four “bands” (Band: 1-5 ranked teams, 6-10, 11-15, […]

All the Assumptions That Are My Life

Statisticians take tours in other people’s data. All methods of statistical inference rest on statistical models. Experiments typically have problems with compliance, measurement error, generalizability to the real world, and representativeness of the sample. Surveys typically have problems of undercoverage, nonresponse, and measurement error. Real surveys are done to learn about the general population. But […]

Jessica Tracy and Alec Beall (authors of the fertile-women-wear-pink study) comment on our Garden of Forking Paths paper, and I comment on their comments

Jessica Tracy and Alec Beall, authors of that paper that claimed that women at peak fertility were more likely to wear red or pink shirts (see further discussion here and here), and then a later paper that claimed that this happens in some weather but not others, just informed me that they have posted a […]

A whole fleet of gremlins: Looking more carefully at Richard Tol’s twice-corrected paper, “The Economic Effects of Climate Change”

We had a discussion the other day of a paper, “The Economic Effects of Climate Change,” by economist Richard Tol. The paper came to my attention after I saw a notice from Adam Marcus that it was recently revised because of data errors. But after looking at the paper more carefully, I see a bunch […]

The gremlins did it? Iffy statistics drive strong policy recommendations

Recently in the sister blog. Yet another chapter in the continuing saga, Don’t Trust Polynomials. P.S. More here.