The Signal and the Noise is a book I had meant to read before sitting in on the Silver & Silver Interview back when I was in Boston for the MIT/ESPN Sports Statistics & Analytics Conference (This One’s for the Books-The SSAC’17 Hackathon and the Aftermath).
It was recommended to me by Mike Wohl, a Houston Rockets Venture Capitalist who makes/made a name for himself by creating a hedge fund using sports statistics/betting.
I read this in the first week back in Dallas in the summer, I’d like to say because I was ambitious but I really just needed to keep my mind from being idle during my semi-annual 4 hour hair appointments.
Anyway, in context to what I was doing concurrently to reading this book, I had just purchased and successfully streamlined a lot of NBA play by play data in SQL and R, and got closer to my goal of finishing my SSAC Research paper.
This book was really dense, loaded with hundreds of case studies that proved Nate Silver’s points when he walked the reader through many kinds of logical fallacies and other incorrect assumptions that statisticians make.
Although it was stats heavy (obviously), I felt like Silver did a great job of weaving his own experiences in his career which fostered an element of inspiration in the book. Also, his voice and the sidebar comments and jokes he made were very unique in revealing his voice.
Another interesting thing he did in the book, perhaps to raise awareness about cultural gender norms, was that every time he referred to a scientist or a doctor he used the female pronoun “she/her,” and at first that threw me off a little because I’m so used to assuming those positions were males.
At the end of the read, I did feel a slight sense of hopelessness for my project because all in all it seemed like there were so many ways to incorrectly forecast data, it made me wonder if there’s even an accurate way.
But to counter that argument, one of the biggest takeaways I took from this book that there is an objective way to capture both quantitative and qualitative data. In the past I only associated “cold hard numbers,” with objective “analyzable” data, but in this book, Silver discussed accurate methods to capture subjective data.
Overall, this book challenged me to think and analyze numbers in a different way, and be more acute when seeing a statistics from sports to the weather.
In addition, since big data is a hot topic in industry right now, while I was traveling I was able to connect with many business men in different walks of their life, after they would initiate conversation with me, having seen the book that I was so engrossed in reading.
I highly recommend this book for any stat geek!