This is a largely reliable and interesting overview of statistical analysis in a variety of fields. Silver has gained popularity in 2008 and last year for his wizardry in aggregating poll numbers and correctly predicting the results of the election. He takes a few jabs at media pundits who are not interested in polls, but insist on manufacturing a narrative which appeals to pre-conceived notions and biases. 
 Silver's interests are broad and eclectic, ranging from Texas Hold 'Em and the stock market to baseball. His use of statistics here is judicious and well-explained. 
 Silver also does a reasonable job explaining statistical concepts to a mass audience, where little more than high-school math is necessary. He does overly use the Bayesian method of statistics -- that is, where probability cannot be interpreted from the event itself, but also from the hypotheses and predictions which preceded it. 
 In politics, Silver separates the wheat from the chaff, throwing out bad guesses with ease. However, he is less careful in the studies of climatology. He refers to bad data sets as reasonable priors, and gives an undue focus to biased sets of data, and misinterprets the recent political focus which is given to environmental science. He overemphasizes the role of controversy in these fields, taking data at face value which in all likelihood is flawed or maybe even manufactured. 
 These omissions are made all the more puzzling due to his reasoned acceptance of other complexities of the field, noting the vast amounts of data to sift through, as well as the non-linear dynamics in meteorology, and noting the difference between climate and weather. One data point does not make or break a broad trend. Because winter is cold, this does not prove or disprove anything on a global scale. 
 In summary, Silver does a fine job at pointing out the many advantages and pointed flaws of statistical analysis. It can be an invaluable tool for dismissing false 'experts', but it is also important to carefully consider the role of prior analysis and carefully setting up your examples.