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The Signal and the Noise: Why So Many Predictions Fail — but Some Don't [Hardcover]

by Nate Silver
4.3 out of 5 stars  See all reviews (759 customer reviews)

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Book Description

September 27, 2012 1594204111 978-1594204111 1
"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."
—Rachel Maddow, author of Drift

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. The New York Times now publishes, where Silver is one of the nation’s most influential political forecasters.

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.

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The Signal and the Noise: Why So Many Predictions Fail — but Some Don't + Thinking, Fast and Slow
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Editorial Reviews Review

Amazon Best Books of the Month, September 2012: People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver's brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Data meets human nature. Baseball, weather forecasting, earthquake prediction, economics, and polling: In all of these areas, Silver finds predictions gone bad thanks to biases, vested interests, and overconfidence. But he also shows where sophisticated forecasters have gotten it right (and occasionally been ignored to boot). In today's metrics-saturated world, Silver's book is a timely and readable reminder that statistics are only as good as the people who wield them. --Darryl Campbell

From Bookforum

Silver doesn't offer one comprehensive theory for what makes a good prediction in his interdisciplinary tour of forecasting. But the book is a useful gloss on the tricky business of making predictions correctly. —Chris Wilson

Product Details

  • Hardcover: 544 pages
  • Publisher: Penguin Press HC, The; 1 edition (September 27, 2012)
  • Language: English
  • ISBN-10: 1594204111
  • ISBN-13: 978-1594204111
  • ASIN: 159420411X
  • Product Dimensions: 2.5 x 3.7 x 0.4 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (759 customer reviews)
  • Amazon Best Sellers Rank: #1,110 in Books (See Top 100 in Books)

More About the Author

Nate Silver is a statistician, writer, and founder of The New York Times political blog Silver also developed PECOTA, a system for forecasting baseball performance that was bought by Baseball Prospectus. He was named one of the world's 100 Most Influential People by Time magazine. He lives in New York.

Customer Reviews

Most Helpful Customer Reviews
609 of 634 people found the following review helpful
Format:Hardcover|Verified Purchase
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.
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202 of 212 people found the following review helpful
5.0 out of 5 stars Great book, and here are some takeaways November 11, 2012
Format:Hardcover|Verified Purchase
Excellent book!!! People looking for a "how to predict" silver bullet will (like some reviewers here) be disappointed, mainly because Silver is too honest to pretend that such a thing exists. The anecdotes and exposition are fantastic, and I wish we could make this book required reading for, say, everyone in the country.

During election season, everyone with a newspaper column or TV show feels entitled to make (transparently partisan) predictions about the consequences of each candidate's election to unemployment/crime/abortion/etc. This kind of pundit chatter, as Silver notes, tends to be insanely inaccurate. But there are also some amazing success stories in the prediction business. I list some chapter-by-chapter takeaways below (though there's obviously a lot depth more to the book than I can fit into a list like this):

1. People have puzzled over prediction and uncertainty for centuries.

2. TV pundits make terrible predictions, no better than random guesses. They are rewarded for being entertaining, and not really penalized for being wrong.

3. Statistics has revolutionized baseball. But computer geeks have not replaced talent scouts altogether. They're working together in more interesting ways now.

4. Weather prediction has gotten lots better over the last fifty years, due to highly sophisticated, large-scale supercomputer modeling.

5. We have almost no ability to predict earthquakes. But we know that some regions are more earthquake prone, and that in a given region an earthquake of magnitude n happens about ten times as often as an earthquake of magnitude (n+1).

6. Economists are terrible at predicting quantities such as next year's GDP. Predictions are only very slightly correlated with reality.
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147 of 162 people found the following review helpful
5.0 out of 5 stars Lively statistics November 7, 2012
By RichB
Format:Hardcover|Verified Purchase
This book explains the unerring accuracy for Nate SIlver's election predictions using Bayesian statistics. The BEST part of the book for me was that I finally understand Bayes' analysis. I used quite a few sophisticated statistical tools in my work (retired as reliability physics expert for semiconductor devices, aka chips), but I was never able to grasp Bayes Theorem until now. Wikipedia's "tutorial" was far too complicated even for a PhD, but Nate provided a simple version that a layman can understand ... and he did it using a hilarious example (look for "cheating"). In fact, I am so impressed with Bayes' analysis that I am thinking about writing a corollary to my two best technical papers grafting a Bayesian view.
Returning to the election prediction issue, consider that each poll of 1000 people has a sampling error of +-5%, easily derived from Poisson statistics. However, when one pools the results from say 25 polls (and removes bias), the sample size is increased by 25-fold, which reduces the sampling error by 5-fold, down to +-1%. Thus, one can make confident predictions over differences FAR smaller than the usual sampling error. When one combines Bayesian pooling with a state-by-state analysis, one can make astonishingly accurate predictions ... Nate predicted ALL 50 states correctly, so his electoral count was exactly on reality as well when fractional electoral counts are eliminated.
Buy the book as it is educational and fun to read.
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515 of 627 people found the following review helpful
2.0 out of 5 stars Disappointing, too much noise, too little insight September 30, 2012
This book was a disappointment for me, and I feel that the time I spent reading it has been mostly wasted. I will first, however, describe what I thought is *good* about the book. Everything in this book is very clear and understandable. As for the content, I think that the idea of Baysean thinking is interesting and sound. The idea is that, whenever making any hypothesis (e.g. a positive mammogram is indicative of breast cancer) into a prediction (for example, that a particular woman with a positive mammogram actually has cancer), one must not forget to estimate all the following three pieces of information:

1. The general prevalence of breast cancer in population. (This is often called the "prior": how likely did you think it was that the woman had cancer before you saw the mammogram)

2. The chance of getting a positive mammogram for a woman with cancer.

3. The chance of getting a positive mammogram for a woman without cancer.

People often tend to ignore items 1 and 3 on the list, leading to very erroneous conclusions. "Bayes rule" is simply a mathematical gadget to combine these three pieces of information and output the prediction (the chance that the particular woman with a positive mammogram has cancer). There is a very detailed explanation of this online (search Google for "yudkowsky on bayes rule"), no worse (if more technical) than the one in the book. If you'd like a less technical description, read chapter 8 of the book (but ignore the rest of it).


Now for the *bad*. While the Baysean idea is valuable, its description would fit in a dozen of pages, and it is certainly insufficient by itself to make good predictions about the real world.
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Most Recent Customer Reviews
5.0 out of 5 stars Great, interesting book
Fascinating book. Written in some ways like "The Tipping Point," but clearer. Each chapter is on a new topic and that topic is used to explore an aspect of predictions.
Published 2 days ago by Lindyhopper
3.0 out of 5 stars Interesting
I found it a bit rough to get thru. I'm not a sports fan so that part was just out me to sleep (but it was my bed time read so that was ok) but it took me a while to wade thru it.
Published 3 days ago by rob0bOy
5.0 out of 5 stars Be a scientist every day
too much superstitious behavior by smart people in the world is costing us all in time and money. too many vitamins, not enough attention to global warming. Read more
Published 4 days ago by joe massey
3.0 out of 5 stars Get it
I bought this for my brother for Christmas and he loves it. I'm really glad it got it for him.
Published 4 days ago by Nathan Richardson
5.0 out of 5 stars Enjoyable read and study on the limits of our ability to make...
I highly recommend this to anyone interested in gaining a deeper understanding of the limits of our ability to make reliable predictions. Read more
Published 5 days ago by Leif C. Ulstrup
4.0 out of 5 stars Bayesian for non-statisticians (like me)
The book starts really well, with the author narrating interesting stories which serves as a background for the ideas he wants to show us. Read more
Published 5 days ago by Elio Asano
5.0 out of 5 stars A Considered Opinion
Looks at data from a couple interesting perspectives and even cites his critics, which I think helped his arguments overall.
Published 14 days ago by J. Justice
4.0 out of 5 stars Fun & an education
Mr. Silver definitely enjoys his own perspective and can draw us in with ease. Anxious to see how his predictions hold up.
Published 17 days ago by Michael A. Strem
5.0 out of 5 stars It's a keeper!
I can't seem to keep it in the house, I keep giving away my copy. Get the paper version, you will read it again and again. Read more
Published 17 days ago by Chris
2.0 out of 5 stars Hindsight biased
The Signal and The Noise has a charming way of introducing a handful of elementary statistical tools while exploring their strengths and weaknesses. Read more
Published 19 days ago by NJ
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Topic From this Discussion
Embarrassing typo "psychics"
yes, someone didn't run spell check. Typos on hardcover pages 124,177,246,379(one you mention) and 402. There are likely more. I enjoyed the book though.
May 27, 2013 by billymac |  See all 2 posts
Typo in figure 1-2?
that really turned me on.
Dec 8, 2012 by Math Lover |  See all 8 posts
Hurricanes vs. Earthquakes
"What would be a more accurate comparison would be the frequency of (all) earthquakes to all precipitation events (hurricanes, rain, mist, fog, ...)"
Earthquakes are felt throughout the globe whereas tropical cyclones are restricted to the tropics. Correlation would be slight. I believe... Read more
May 26, 2013 by David F. Mcginnis |  See all 2 posts
Why was geologic data omitted from Chapter 12?
I just finished the book. I believe he chose 1850 because it was the beginning of the sharp increase in anthropogenic CO2, which was the subject under discussion, and is often chosen for comparison by a sort of convention.
May 26, 2013 by David F. Mcginnis |  See all 2 posts
Figure 9-2 chess position error
Also note that the figure claims to be the position after three white moves, it is actually after two black moves.
Jan 7, 2013 by Nathan Pettengill |  See all 3 posts
Figure 1-1 Page 21 Be the first to reply
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