Interesting book about forecasting, statistics, and why simply having more data is not going to result in better predictions. The writing is mostly entertaining and accessible to all, but if you're interested in the details there's enough there that I was able to correctly answer 3/4 questions on a Bayesian theory test a friend coincidentally posted on Facebook while I was reading this book.

The direction seems a little scattered though, it's more like a series of case studies or vignettes without a clear and cohesive direction. The most important information in the book (in my opinion) is Bayesian theory and how we can and should use it to keep our forecasts realistic; yet it isn't mentioned till over half way into the book and then isn't consistently emphasized through till the end. The rest of the book is examples of predictions gone right or wrong and examinations why; interesting but a little disjointed seeming at times. Still, very interesting read and worth picking up.