I PREDICT THAT ONE DAY Nate Silver will be remembered for doing something more consequential than forecasting the winners of presidential elections.

Do I have a sophisticated statistical model like Silver’s FiveThirtyEight backing my prediction? No, nor do I have a computer running thousands of scenarios a day. But Silver’s new book, The Signal and the Noise: Why So Many Predictions Fail — But Some Don’t, suggests that the true value of forecasting the future won’t be found among the high-powered processors that populate the promised land of “Big Data.” 

Instead, Silver portrays forecasting as a humanistic heuristic any of us can use to grow closer to truth. Indeed, he makes the case that we must. “The numbers have no way of speaking for themselves,” he argues from the outset of the book. “We speak for them. We imbue them with meaning.” 

Guided by this ethos, Silver uses The Signal and the Noise to discern the highest and best use of data in a variety of disciplines, from meteorology and epidemiology to baseball and poker. But notably absent is a critical discussion of what greater goal such data-driven divination serves in politics, the area in which Silver currently enjoys the greatest notoriety. 

“[M]en may construe things after their fashion / Clean from the purpose of the things themselves,” Shakespeare’s Cicero warned, and Silver repeats in his introduction. But what purpose is served by a program that perfectly predicts the outcome of a presidential race if such a forecast does not aid the republic it models?

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Silver explains in the introduction to The Signal and the Noise that he hopes his book will be more than another story of “nerds conquering the world” with numbers, à la Moneyball or Freakonomics. But it’s not hard to see the appeal of such a frame for Silver’s life story. 

Bored by a job at the accounting firm KPMG in 2003, Silver cooked up PECOTA, a new system for predicting the performance of baseball players (particularly pitchers) over the course of their lifetimes. A key differentiator of Silver’s model was that instead of making a single, straightforward prediction, it ranked the likelihood of several potential outcomes for each player — multiple scenarios, multiple probabilities.

This switch in methodology foreshadows all of the theoretical arguments that Silver’s book and subsequent career make. By developing a model that yielded a series of guesses, Silver was drawing upon a statistical tradition that started with Thomas Bayes, an 18th century English clergyman. Bayes is known for a treatise that proposed a mathematical method for making predictions about phenomena, then incorporating the outcomes into an ever-improving probabilistic model. Bayes’s ideas were later taken up by the French mathematician and astronomer Pierre-Simon Laplace, who used them to make scientific predictions about the cosmos in an era before orbital telescopes and supercomputers provided greater certainty.

Eventually Bayes’s ideas fell out of favor among mathematicians. The statistical method used by most scientists today, for example, is based instead on the ideas of the E...