stoking the confirmation biases of their customers, which has been shown by the likes of Dick Morris and Karl Rove claiming to “have the math” only to be embarrassingly wrong on the outcome of recent elections. But they aren’t embarrassed-- they aren’t paid to be correct, only to confirm bias.
Silver also uses the baseball player Dustin Pedroia as an example of the failure of Big Data. On paper, or more accurately, using computer stats, a guy like Pedroia should never have made it into Major League baseball: he’s too small, too slow, not a very powerful hitter and can’t throw very well. Instead of failing, Pedroia is an all-star and a winner. What gives? There are intangibles, data is never able to be completely known. All the stats in the world cannot always be interpreted perfectly.
Separating what’s important (the signal) from confusing interference (the noise) is the key to forecasting any natural event or human phenomenon. We are good at forecasting some things like weather and hurricanes, but poor at predicting other things like earthquakes and terror attacks. Silver is an excellent explainer of how we know things and the limitations of that knowledge. He uses a wide-ranging array of interesting stories: from bird flu and climate to Donald Rumsfeld and poker. Refreshingly, I find no ideological or political preferences in his discussion.
This book is highly recommended.