At last, I have finished reading the book 'the signal and the noise: why so many predictions fail- but some don't'. It does a very good job in explaining 1) why it is important for us to understand uncertainty - the central theme of statistics -, 2) why statistical analysis is so challenging, and 3) how we can (sometimes) improve the model, in plain English (that is, without statistical jargons). This book shines the most in its careful selection of problems it discusses; baseball, earthquake, stock market, chess, and terrorism are very good examples which shows different aspects of the 'prediction problem'.
I would strongly recommend this book to those who are interested in understanding why people are making such a big fuss about Big Data/machine learning/statistics. As Larry Wasserman pointed out in his blog post, however, its treatment of frequentist statistics is very unfair... and I feel very uncomfortable every time a Bayesian claims that Bayesian statistics is a magic bullet to every problem frequentist statistics has. But this is a pop science book after all... probably it was a necessary sacrifice to deliver the idea to non-academics.
I would strongly recommend this book to those who are interested in understanding why people are making such a big fuss about Big Data/machine learning/statistics. As Larry Wasserman pointed out in his blog post, however, its treatment of frequentist statistics is very unfair... and I feel very uncomfortable every time a Bayesian claims that Bayesian statistics is a magic bullet to every problem frequentist statistics has. But this is a pop science book after all... probably it was a necessary sacrifice to deliver the idea to non-academics.
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