Nate Silver has done an incredible (and, quite possibly an unpredictable) thing with _The Signal and the Noise_: He has written an extremely good book when he didn't even have to. Nothing is more common than for someone like Silver--a media phenom with a strong platform (his 538 blog) to phone a book in to cash in on his 15 minutes. I have probably read two dozen books in the past five years that do exactly this. But _The Signal and the Noise_ is a much more substantial book than, say, _The Black Swan_ or either of the _Freakonomics_ offerings. It is a wide-ranging, in-depth look at the ways that we are wired to make predictions (and the reasons that these are so often wrong).

Silver ranges over a variety of prediction environments: baseball, chess, poker, the stock market, politics, weather, and terrorist attacks to name the most interesting. Throughout it all, he reminds us that human beings are pattern-seeking animals and that we are just as likely to build patterns where none exist as we are to find the correct patterns and harness their predictive capacity. Predictions work best when they are 1) probabilistic (i.e., express a range of possibilities and assign probabilities for each); 2) when they use as much information--both statistical and analytical--as possible; and 3) when they are continually revised to account for new information.

As logical as these sound, human nature seems to drive us in three opposite directions: 1) we seek predictions that are definite and can be acted upon (i.e. "Obama will beat Romney," or "it will rain tomorrow"); 2) we gravitate towards methodologies that seem to discover a magic bullet formula that guarantees success; and 3) we feel compelled to stand by our predictions even as they become increasingly unlikely. Seasoned prognosticators play a long game. Under the right circumstances (a poker game, for example), a strategy that produces only a sightly better prediction than random chance can produce huge dividends.

Perhaps most surprisingly, Silver is a great writer (or, at least a great explainer). As an English major with very little grounding in statistics, I could still understand everything he said. Even more importantly, his narratives are interesting. Who could have predicted that from America's most famous stat-geek?