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The Signal and the Noise: Why So Many Predictions Fail - But Some Don't 1st Edition

4.3 out of 5 stars 1,034 customer reviews
ISBN-13: 978-1594204111
ISBN-10: 159420411X
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Editorial Reviews

Amazon.com 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
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Product Details

  • Hardcover: 544 pages
  • Publisher: Penguin Press; 1 edition (September 27, 2012)
  • Language: English
  • ISBN-10: 159420411X
  • ISBN-13: 978-1594204111
  • Product Dimensions: 6.4 x 1.2 x 9.5 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (1,034 customer reviews)
  • Amazon Best Sellers Rank: #12,598 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

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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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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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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Format: Hardcover
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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