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Best Books of the Month
Want to know our Editors' picks for the best books of the month? Browse Best Books of the Month, featuring our favorite new books in more than a dozen categories.
This is the best general-readership book on applied statistics that I've read. Short review: if you're interested in science, economics, or prediction: read it. It's full of interesting cases, builds intuition, and is a readable example of Bayesian thinking.
Longer review: I'm an applied business researcher and that means my job is to deliver quality forecasts: to make them, persuade people of them, and live by the results they bring. Silver's new book offers a wealth of insight for many different audiences. It will help you to develop intuition for the kinds of predictions that are possible, that are not so possible, where they may go wrong, and how to avoid some common pitfalls.
The core concept is this: prediction is a vital part of science, of business, of politics, of pretty much everything we do. But we're not very good at it, and fall prey to cognitive biases and other systemic problems such as information overload that make things worse. However, we are simultaneously learning more about how such things occur and that knowledge can be used to make predictions better -- and to improve our models in science, politics, business, medicine, and so many other areas.
The book presents real-world experience and critical reflection on what happens to research in social contexts. Data-driven models with inadequate theory can lead to terrible inferences. For example, on p. 162: "What happens in systems with noisy data and underdeveloped theory - like earthquake prediction and parts of economic and political science - is a two-step process. First, people start to mistake the noise for a signal. Second, this noise pollutes journals, blogs, and news accounts with false alarms, undermining good science and setting back our ability to understand how the system really works.Read more ›
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20 of 25 people found the following review helpful
This book is especially interesting because Nate Silver has honed firsthand his statistical skills onto numerous domains including professional poker, baseball performance forecasting (he developed one of the best software program to do that), political elections (his "fivethirtyeight" blog). And, when he is not a firsthand practitioner he is a first class investigator.
The first seven chapters cover the errors and successes people have had in forecasting in various disciplines. Chapter eight is the most pedagogical, as the author explains the basics of Bayes Theorem that he considers as an overall solution to many of the errors we make in forecasting. The last five chapters focus on Bayesian thinking within various disciplines.
Nate Silver's coverage of the credit rating agencies "Catastrophic failure of prediction" (first chapter title) is excellent.Read more ›
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A consistently successful bettor, to use the terminology of the book, has to possess subjective probability estimates (his Bayesian priors) that capture more signal than the market consensus. Thus one of the factors in beating any market hinges upon the efficiency of the market in question. As Silver details the on-line poker market from 2003 to October 2006 was not efficient; while nowhere near the average incompetence of the poker market, pre-Moneyball baseball and political forecasting were also less than efficient. Mr. Silver notes he was fortunate to make his reputation in markets "where the water level was set pretty low."
An example of a market where the water level is high would be the stock market. There are large armies of highly trained analysts working for institutions with 50 million dollar budgets and large computers pouring over data. Yet there remains the problem of how to explain bubbles: Silver covers a number of possible explanations none of which is fully satisfactory.
I will spend the rest of this review going over Silver's view on climate change and its troubled politics. There is solid scientific research that demonstrates throughout earth's history temperature has fluctuated significantly. Scientists possess a solid theory which explains these fluctuations: changes in the amounts of greenhouse gases (carbon dioxide, water vapor, methane) in the atmosphere. With a rise in greenhouse gases the earth's surface becomes warmer.
Where controversy arises is trying to put specific numbers to the temperature rise associated with a given carbon dioxide increase. Such a prediction requires estimates of complex interactions between many variables.Read more ›
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This item: The Signal and the Noise: Why So Many Predictions Fail - But Some Don't
