Prediction and uncertainty

Posted: 30 October 2012 in Uncategorized
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Readers of this blog know that I have sung the praises of Nate Silver’s statistical work for quite some time, long before the right-wing attacks on his electoral predictions started.

My appreciation stems not from Silver’s predictions per se but, rather, from his appreciation of uncertainty, as this review by Samuel Popkin makes clear.

One of the biggest problems we have in separating signal from noise is that when we look too hard for certainty that isn’t there, we often end up attracted to noise, either because it is more prominent or because it confirms what we would like to believe. This is a worse problem in politics than in baseball or poker. If most polls are reporting a tight race, an outlier showing a bigger gap will be the poll that makes news, thus getting more of our attention. Partisan TV pundits try to assuage the worries of the faithful on their side instead of making accurate predictions. When Silver analyzed 1,000 predictions on The McClaughlin Group, he found them no more accurate than flipping a coin. . .

In his analysis of fascinating examples ranging across all the areas in which we try to predict future outcomes, Silver stresses the gap between what we claim to know and what we actually know. In 1997, the National Weather Service predicted that heavy winter snows would cause North Dakota’s Red River to flood over its banks in two months, cresting at 49 feet. The residents of Grand Forks were relieved, since their levees were designed to withstand a 51-foot crest. If the Weather Service had mentioned that the margin of error for its forecast was five feet, the three feet of water that poured over the levels in an eventual 54-foot crest might not have destroyed 75 percent of the town. Happily, the Weather Service now provides that information—an example of an easy reform to forecasting.

  1. […] especially poll-based modelers, like one of the best, Nate Silver, as I’ve discussed here and here) always say they’re recognizing and capturing uncertainty, for example, in the “error […]

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