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The Signal and the Noise: The Art and Science of Prediction Paperback – 18 Apr 2013

3.9 out of 5 stars 142 customer reviews

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Product details

  • Paperback: 544 pages
  • Publisher: Penguin (18 April 2013)
  • Language: English
  • ISBN-10: 0141975652
  • ISBN-13: 978-0141975658
  • Product Dimensions: 12.9 x 2.3 x 19.8 cm
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (142 customer reviews)
  • Amazon Bestsellers Rank: 11,401 in Books (See Top 100 in Books)

Product Description

Review

Outstanding... I was hooked (Tim Harford Financial Times)

One of the more momentous books of the decade (The New York Times Book Review)

A lucid explanation of how to think probabilistically (Guardian)

The inhabitants of Westminster are speed-reading The Signal and the Noise... They will find the book remarkable and rewarding (Sunday Telegraph)

Is there anything now that Nate Silver could tell us that we wouldn't believe? (Jonathan Freedland)

Fascinating... our age's Brunel (Bryan Appleyard Sunday Times)

A surprisingly accessible peek into the world of mathematical probability (Daily Telegraph)

The Galileo of number crunchers (Independent)

A 34-year old Delphic Oracle (Daily Beast)

About the Author

Nate Silver is a statistician and political forecaster at The New York Times. In 2012, he correctly predicted the outcome of 50 out of 50 states during the US presidential election, trumping the professional pollsters and pundits. He was named one of TIME's 100 Most Influential People in the world, and one of Rolling Stones' top Agents of Change. He lives in Brooklyn, New York.


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Customer Reviews

Top Customer Reviews

Format: Hardcover Verified Purchase
Mr Silver clearly knows what he is talking about, but I'm less sure he knows how to talk about it. I assume he set out to write a chatty, non-challenging book, but the result is light on substance and structure.

The Nobel prize-winning physicist Niels Bohr famously said 'Prediction is very difficult, especially if it's about the future'. This pretty much sums up the first half of the book. Yes, the detail about the financial crisis, weather forecasting, earthquakes etc is mildly interesting, but in relation to prediction, you will be wading through a lot of noise to extract the signal ('human nature makes us over-confident predictors', 'without either good theory or good empirical data, you may as well just guess','the most confident pundits are usually the worst' etc).

The substance of the book comes in twenty pages in the middle, where Silver introduces Bayesian logic (I learnt in maths classes at school when I was fourteen so it wasn't new to me, and it doesn't need 200 pages of build up). The best section is where Silver contrasts Bayesian logic to Fisherian logic. Fisher created the maths that is used almost universally in medical and social science research to prove the efficacy of a treatment or theory. Silver explains how flawed this maths is - which is presumably why two thirds of the positive findings claimed in medical journals cannot be replicated. This is pretty heady stuff.

Silver claims that the second half of the book is about how to make predictions better. It is mostly more examples of failure, this time in chess, investment, climate and terrorism, with a few asides that might be considered signals ('testing is good', 'groups/markets tend to make better predictions than individuals').
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Format: Hardcover Verified Purchase
This is a book about prediction and the use of statistics to forecast future events such as earthquakes and the outcome of elections. When it's good it's a lucid and enjoyable read which makes some important points about the art of prediction, with the chapters on political punditry and economic forecasting stand out as especially good. Unfortunately this is let down by a number of problems. These include the interminable and really quite tedious chapters on poker, baseball and chess (I really don't know why the chess one is in the book at all), and the inclusion of a number of serious errors and misconceptions in the chapter on epidemiology. This last is a subject that I think I have some knowledge of, and it's disturbing to see straightforward and important factual errors - the definition of the basic reproductive rate used is badly wrong, for example (if anyone's interested the correct definition is that it is the number of new infections produced by a single infectious host *in a population of completely susceptible hosts*), and the interpretation is also wrong (it's not correct that any disease with basic reproductive rate >1 will go on to infect all susceptible hosts in the population). These are not nit-picking little errors - it's the equivalent of getting the definition of interest rate seriously wrong in a discussion of economics. These are fundamental concepts and the errors tell us that the author did not properly understand the subject that he's writing about.Read more ›
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Format: Paperback Verified Purchase
If you are really into measuring signals in noise this isn't for you. If you want to be told, page after page, how brilliant the author is at, for example, football (US) statistics then you will find something of interest. Don't expect to find any useful information on regression; Bayes; predictor-corrector; Kalman; entropy; ... or just about anything to do with prediction.
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Format: Hardcover Verified Purchase
Silver has some good ideas, and he is to be commended for scruplously footnoting his references, but there are some mistakes (the "cows would rate this" was from an S&P analyst,not Moody's) and he utilises heuristics he criticises elsewhere (lazily claiming the industrial revolution happened, just like that, in 1775 with the excuse "it is a nice round number").

My two main criticisms for non Americna readers is that it is quite US centric (I don't care about baseball, and the general moneyball story is impossible to avoid) and the main philosophical stuff (which was most useful and ineresting to me) makes up a small portion of the book, the majority with various examples where he makes the same arguments with interview of different people that are somewhat non-questioning.

He gives some useful examples throughout the book, covering meteorology, earthquakes, transmision of viruses, but it still feels as if it could have been cut. The stuff on Bayes is interetsing but really skates over the issue of how you come up with a Bayesian prior when you can't iteratively improve them because you do not have many data points. Given the time he spends looking at the financial crisis, this is a flaw as it reduces the "wow, Bayes is really useful" impact when it cannot offer that much resolution to the problem of predicting economic and financial crises, the key predictive failure he cites.

Even so, as a way of getting people to think a bit more deeply about what it is to make a prediction and how to know if it was well constructed, and how to integrate concepts of epistemology, it is a useful introductory book.
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