I've just finished reading Nate Silver's The Signal and the Noise, with mixed impressions. On the one hand, it is overall a pleasant read and the first best-seller starring Bayes' theorem (I wrote about this point before). Any book that keeps professional statisticians busy guessing whether the author is a true Bayesian or a frequentist in disguise can't be all bad! On the other hand, there are some disappointing points (discussed below.) Concentrating on the negative side might seem unfair, but should also be more interesting, since the reviews so far have mostly been elogious.
- The style is conversational and easy to follow, but a few details are annoying, such as Silver's love for adverbs: "literally" (used on almost every page, sometimes more than once) "essentially" (for "almost") and "incredibly" (for "very"). Mixed metaphors and chained clichés are not uncommon. I would have expected more thorough editing for what was clearly a potential best-seller.
- One gets the impression that Silver proposes a technical solution to a social (and political) problem. This is (very briefly) touched upon by a review in Science1 and, more incisively, here. In the Introduction, he says: ... a single lax assumption in the credit ratings agencies' models played a huge role in bringing down the whole global financial system.(namely, that mortgage defaults will be uncorrelated). Although he does nuance his position throughout the book, he does not consider the stark fact that the ratings agencies have a strong financial incentive to make the most favorable prediction, not the most accurate one. In fact, they are paid to certify a given version of the future, without having to bet on it. This problem cannot be solved by pushing them to adopt more elaborate (and thus even easier to fudge) prediction techniques, but rather by changing the reward mechanism.