The Signal and the Noise: Why So Many Predictions Fail--but Some Don't

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		The Signal and the Noise: Why So Many Predictions Fail--but Some Don't
		The Signal and the Noise: Why So Many Predictions Fail--but Some Don't

One of Wall Street Journal's Best Ten Works of Nonfiction in 2012
 
New York Times Bestseller

“Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade.”
—New York Times Book Review
 
"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."
—Rachel Maddow, author of Drift

"A serious treatise about the craft of prediction—without academic mathematics—cheerily aimed at lay readers. Silver's coverage is polymathic, ranging from poker and earthquakes to climate change and terrorism."
—New York Review of Books

Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com.
 
Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.

In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.

Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.

With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.

网友对The Signal and the Noise: Why So Many Predictions Fail--but Some Don't的评论

这是学习数据挖掘人的必读之书。英文版保持了作者的写作原貌，能够更好的体会作者在该技术领域的思考和功力。
值得数据挖掘及相关工程人员阅读。

美国神棍NATE基于统计学等资料对美国总统大选进行了成功预测，这本书就是讲的相关内容。早就想一睹为快了，终于等到亚马逊降价到了99，立马下手。

包装不错，送货速度不错。书品相还行，是新的，慢慢看。

十分满意！！中英文版本都买了。

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." This is the kind of insight that every good practitioner acquires through hard-won battles, and continues to wrestle every day both in doing work and in communicating it to others.

It is both readable and technically accurate: it presents just enough model details yet avoids being formula-heavy. Statisticians will be able to reproduce models similar to the ones he discusses, but general readers will not be left out: the material is clear and applicable. Scholars of all stripes will appreciate the copious notes and citations, 56 pages of notes and another 20 pages of index, which detail the many sources. It is also important to note that this is perhaps the best general readership book from a Bayesian perspective -- a viewpoint that is overdue for readable exposition.

The models cover a diversity of areas from baseball to politics, from earthquakes to finance, from climate science to chess. Of course this makes the book fascinating to generalists, geeks, and breadth thinkers, but perhaps more importantly, I think it serves well to develop reusable intuition across domains. And, for those of us who practice such things professionally, to bring stories and examples that we can tell and use to illustrate concepts with the people we inform.

There are three audiences who might not appreciate the book as much. First are students looking for a how-to book. Silver provides a lot of pointers and examples, but does not get into nuts and bolts details or supply foundational technical instruction. That requires coursework in research methods and and statistics. Second, his approach to doing multiple models and interpreting them humbly will not satisfy those who promote a naive, gee-whiz, "look how great these new methods are" approach to research. But then, that's not a problem; it's a good thing. The third non-fitting audience will be experts who desire depth in one of the book's many topic areas; it's not a technical treatise for them and I can confidently predict grumbling in some quarters. Overall, those three audiences are small, which happily leaves the rest of us to enjoy the book.

What would make it better? As a pro, I'd like a little more depth (of course). It emphasizes games a little too much for my taste. And a clearer prescriptive framework could be nice (but also could be a problem for reasons he illustrates). But those are minor points; it hits its target better than any other such book I know.

Conclusion: if you're interested in scientific or statistical forecasting, either as a professional or layperson, or if you simply enjoy general science books, get it. Cheers!

Nate Silver is best known for using polling data to call political elections. He missed on the Trump win, but was pretty good up until then.

The Signal and the Noise is a well written, well researched and well reasoned book about forecasting and the various mistakes that prognosticators make. He addresses failures as the inability of economists and others to foresee the bursting of the housing bubble and the chaos it created in 2008. Other themes include easier-to-predict subjects such as future performance of major league baseball players and the success (or not) of poker players. In these later two, he has real world experience as he developed software to predict baseball player performance and made a living as a professional poker player.

Other forecasting areas that he writes about include weather (a modern success); earthquakes (not so much due to difficulties in differentiating the signal from the noise); the spread of infectious diseases (difficult to model due to human behaviour); and climate change (right on warming but uncertain about effects).

One of the over all themes involves the Bayes Theorem. This requires an a priori hunch about the chances of an event that is refined by future observations and experimenting.

There were sections I like more than others, but this may correlate more with my affinity for the subjects rather than Silver's reporting. I particularly like the section on Climate Change research. It was thoughtful and open-minded. As he does throughout the book, he looks at the facts and the stats and interviews the people involved in the research.

Early in "The Signal and the Noise," author Nate Silver reminds us how often we make predictions and forecasts in our day-to-day lives. Doing so is an unavoidable task of living, and this superb book takes the reader through the pitfalls inherent in prognostication and how better to avoid them by recognizing what is irrelevant (the noise) and what is germane (the signal).

There are many types of errors that lead to incorrect forecasts, and Silver discusses how people let their biases overly control their thoughts, how people use incomplete information to come up with predictions, and how they ignore pertinent warning signs. Silver outlines personality traits that make for both good and bad forecasters and talks about the types of errors that lead to incorrect predictions and the importance of objectivity.

The author shows that the concept behind Bayes' theorem, thinking probabilistically, is the key in revising our predictions as we get new information to make them better. Silver takes the reader on a breezy ride through the fields of politics, the housing bubble of the last decade, baseball, weather forecasting, earthquakes, economics, pandemics, gambling, chess, poker, stock markets, climate change, and terrorism to illustrate the concepts he puts forth.

Silver acknowledges that luck plays a role in some areas of our lives, but stresses that being more fundamentally sound in our prediction abilities can always help us ignore the noise, pay attention to the signal, and make better decisions. He closes with a short recapitulation of the main concepts he introduces.

The author is a well-known liberal, but he is an honest broker and teaches his concepts without making the case for any political ideology. "The Signal and the Noise" is great fun, especially for math lovers, but also for anyone with an interest in one of the areas he covers in the thirteen chapters he uses to illustrate his concepts. One prediction I can be sure in seeing come true is that the vast majority who invest the time to read this book will be glad that they did so.

I bought this based on a review I'd read in the WSJ and by a reviewer I trusted. A mistake.

While it is comparitively well written and I enjoyed the writing, the author fails to really answer the why. It merely demonstrates failures, usually with interesting baseball examples, but most often with the same player. I finally got tired of seeing why they made bad predictions about a player the author loved.

Yes, the author explains that there was not enough data measured for various predictions, in particular the drive of a player or politician, or the prevailing winds in a political race. But, I think the entire book boils down to a few pages explaining various cases of immeasurable intangibles that make predicting hazardous and treacherous. Intangibles that can change predictions greatly, e.g., there were three economists in a room ...

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