I'm a corporate training consultant and adjunct professor who loves to read! I'm always looking for the next big thing.
I had been wanting to read this book since it was released, and I finally had the chance. I wasn't disappointed with it. In this book, the author takes a non-mathematical approach to understanding how statistics enable predictions to be made. Instead of talking about actual formulas or complex theories, he tells stories that give examples of predictions that have failed and those that have been successful. Personally, I would have liked to have been exposed to more of the math; however, I recognize that it would not be for everyone.
The book is divided into 13 chapters, and each chapter aligns with a different example of predictions that are successful and those that are not (more or less). Admittedly, some of the chapters were less interesting to me than others. Nevertheless, I feel as though I was able to learn some very important concepts in each chapter. For example, the author's background is in statistics, and he made a name for himself with baseball statistics. I never thought much about baseball statistics, yet I learned something incredibly valuable from this chapter. The author made a comparison between baseball predictions and presidential election predictions. I would have thought that presidential elections would be far richer in data (because of the magnitude of important); however, that is far, far from the truth. Presidential elections happen only once every four years--and there have been only 57 presidential elections in the entire history of the United States. In contrast, there are more than 57 games of baseball played every single year. The dataset in baseball is insanely rich, and what we learn about predictions in baseball can carry over into other data-rich fields.
Another field that is rich in data is weather prediction. I have never wanted to become a meteorologist in my life. I struggle with getting predictions wrong so often. Even so, this chapter was fascinating because the author describes why it's far easier to predict good weather than it is to predict bad weather. It is those bad weather predictions that seem to go wrong so often that make people question the skills of meteorologists, yet it is statistically less inaccurate than we might think. Another thing that I learned in this chapter is that there is a very, very big difference between meteorology and climatology. Climatology attempts to predict weather patterns over many, many years (e.g. 60 to 100); however, meteorology attempts to predict daily weather patterns. Over the longer duration, it is less challenging to predict weather patterns. It is far more difficult to predict daily fluctuations than it is to predict long-term trends. Incidentally, this heuristic holds true in other fields that the author described in other chapters. The stock market is a perfect example of this. The long-term trends in the stock market are much easier to predict than the daily fluctuations.
Far and beyond, my favorite chapter was the one that covered the game of chess. Chess is my favorite game by far. I am fascinated by the game because, unlike poker (described in a separate chapter of the book), you know everything that your opponent knows. All of the pieces are on the board in front of both players. There are no cards that are being held in your opponent’s hand that you have to guess about. Moreover, you know every possible move that is allowed by both you and your opponent. Even so, with all of that knowledge, people still lose at chess. It seems inconceivable that there were ever be anything other than a draw, yet it happens all the time. Good players win. The author talks a lot about how difficult it is to make predictions about the best play in chess because humans are only able to think about two or three moves at a time. Those players who can think out longer moves seem to do better. Enter the computer. Computers have the ability to calculate more moves in less time than humans. This provides computers with far better predictive power than humans. After Deep Blue defeated Garry Kasparov in 1997, the face of computer chess changed significantly. The author predicts that a human will never again be able to defeat a computer at chess (at that level of competition).
In the end, this last example is what seems to be the framework on which the entire book is built. Technology has changed the way in which we make predictions. Computers have the ability to process more data than ever before. And more data exist than ever before! New fields, like data analytics and big data, are pushing the boundaries of what computers can do with large datasets and their utility in prediction. Of course, some systems (like the weather) are less predictable than others (like chess); however, technology is enabling us to get closer and closer to more precise predictions. The author feels as though this ongoing advance in data and technology will ultimately be helpful in more and more fields include homeland security and the war on terror. I, for one, can't wait to see where it leads us.
Nate Silver is hot right now. As I write this, it is three days before the presidential election and he is predicting an Obama win (82% chance of winning). His insights about stats, opinions, signal and noise are spot on. Although I am still not 100% sure what Bayesian logic is. Overall a great listen full of insight. A note on the narrator. I take back every negative comment I've ever made in my reviews of his performances. He was excellent in this context.
I would like to listen to parts of it again. There are some sections which had far too much detail for me, but at the same time, that is likely a plus for others.
I enjoy the parts about how he comes up with his predictions - which I thought was a bit light in the book. I would have liked more insight on the political polling - but maybe that will be the content for his next book - which I will also buy.
It is a tough read and a long book - but it is worth it. If you like predictive math and logic - and want some insight into the future of market research - it is quite valuable.
This book shows how perceptive people have separated relevant data from irrelevant data.
The author is fond of baseball and baseball statistics. Not being fond of baseball, I had to remind myself from time to time that the book is about data and analyzing data and not about baseball. Sometimes the book moves a little slow, but it is full of good information.
This is not a mathematical book about statistics so much as a book about judgment calls and how to evaluate relevance.
The author's view on Bayes's theorem is particularly interesting. I hope he will elaborate on Bayes's theorem with examples in detail in a future book.
Reviewing the mortgage crisis and the responsible participants was very interesting, and the fact that a hybrid approach to probabilities (number analysis and expert opinion combined) was superior to the pure Moneyball approach was certainly revealing and practical.I also now appreciate much more the work and efforts of the national weather service, what they're able to accomplish, and why their mission should be important to all of us.
Heck, I thought it was Nate Silver the whole time. I'm sorry, Mike! I guess that's how authentic it seemed.
I really enjoyed the content and insight it provided, so I burned through it at 3x speed.
Really enjoyed it!
If your looking for how Nate did some great research and did some very good successes I recommend the book. If you like a story with drama, mystery and suspense it is a little dry and not for you.
Sensible, thought-provoking, data-based
The examples were very relatable and understandable.
The baseball example was the most illustrative.
Seek the truth...
I focus on fiction, sci-fi, fantasy, science, history, politics and read a lot. I try to review everything I read.
I love statistics and data analysis and Nate Silver is very smart and knows a lot about data analysis, yet Nate Silver seems to have an “unhealthy obsession” with data analysis. There are quite a number of interesting bits in The Signal and the Noise but it starts with a long and in depth discussion of baseball statistics, which (if you are apathetic to baseball) is only mildly interesting at best. The overall theme of this book was to show the value of Bayesian statistics. I am a big fan of Bayesian stats, but the descriptions here seem weak and the author sometimes seems to delve into data analysis just for sake of data analysis. It felt a bit like being in the room with an obsessive compulsive geek with a calculator. You may learn a lot, but it might not be a lot of fun.
This book's release coincided roughly with the author's correctly calling the Obama reelection, and I know there was some promotional buzz. I enjoyed the book, but like so many others, it is laden with all sorts of random anecdotes and stories that don't really cohere well, so it serves as a very light repast of actionable, penetrating statistical methods, slathered over with plenty of entertainment and filler. So, it probably nicely fulfills the contemporary book industry formula for popular writing and its marketing.
I haven't read the print version; I'm a listener exclusively.
Good to Great, Jim Collins: The idea, that research and analysis is key, before a conclusion can be drawn is a theme in these books.
He has a very good reading voice. I'm not sure if I've listened to him before, and as a reader he doesn't stand out among the good readers, but he's definitely in that group.
This isn't that kind of book; there is no story for to film.
This was exciting to listen to; I really appreciate good analysis before conclusions are drawn, and I feel like Nate did a great job in applying his claimed principles throughout the book.