Errors, blunders & lies

David S. Salsburg, author of “The Lady Tasting Tea”*, which I enjoyed greatly, hits the spot again with his new book on Errors, Blunders & Lies-How to Tell the Difference. It’s all about a fundamental statistical equation: Observation = model + error. The errors, of course, are normal and must be expected. But blunders and lies cannot be tolerated.

The section on errors concludes with my favorite chapter: “Regression and Big Data”. There Salsburg endorses my favorite way to avoid over-fitting of happenstance results—hold back at random 10 percent of the data and see how well these outcomes are predicted by the 90 percent you regress.** Whenever I tried this on manufacturing data it became very clear that our high-powered statistical models worked very well for predicting what happened last month. 😉 They were worthless for seeing into the future.

Another personal favorite is the bit on spurious correlations that Italian statistician Carlo Bonferroni*** guarded against, also known as the “will of the wisps” per the founder of Yale’s statistics school—Francis Anscombe.

If you are looking for statistical insights that come without all the dreary mathematical details, this book on “Errors, Blunders & Lies” will be just the ticket. Salsburg concludes with a timely heads-up on the statistical lies caused “curbstoning” (reported here by the New York Post), which may soon combine with gerrymandering (see my previous post) to create a perfect storm of data tampering in the upcoming census. We’d all do well to sharpen up our savvy on stats!

The old saying is that “figures will not lie,” but a new saying is “liars will figure.” It is our duty, as practical statisticians, to prevent the liar from figuring; in other words, to prevent him from perverting the truth, in the interest of some theory he wishes to establish.

– Carroll D. Wright, U.S. government statistician, speaking to 1889 Convention of Commissioners of Bureaus of Statistics of Labor.

*Based on the story told here.

**An idea attributed to the inventor of modern day statistics—R. A. Fisher, and endorsed by famed mathematician John Tukey, who suggested the hold-back be 10 percent.

***See my blog on Bonferroni of Bergamo.

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