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The signal and the noise : why most…

The signal and the noise : why most predictions fail but some don't (edition 2012)

by Nate Silver

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1,160447,004 (3.98)22
Title:The signal and the noise : why most predictions fail but some don't
Authors:Nate Silver
Info:New York : Penguin Press, 2012.
Collections:Read but unowned
Tags:nonfiction, statistics, library

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The Signal and the Noise: Why So Many Predictions Fail — but Some Don't by Nate Silver

  1. 20
    Thinking, Fast and Slow by Daniel Kahneman (BenTreat)
    BenTreat: Integrates some of the analytical techniques Silver describes with common irrational patterns of decision-making; Kahneman's book explains how to use some of Silver's techniques (and other tools) to avoid making decisions which are not in one's own best interest.… (more)

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Showing 1-5 of 42 (next | show all)
Have you ever wondered how to make good predictions about difficult-to-predict phenomena? Can you handle occasionally dry descriptions of statistical models and predictive strategies? Read this book.

That said, I agreed with this book more often than I enjoyed it. The topic of predictions and the information about them is great, but I found many of the specific examples uninteresting. Baseball, for instance, is meaningless and dull to me. Climate change, weather patterns, and earthquake predictions are not topics that I'd seek out detailed discussions about, but in this context they were alright. The sections dealing with predictions about politics, terrorism, and disease pandemics were much more interesting to me.

( )
  wishanem | Jan 27, 2015 |
A very good book on forecasting and Bayesian thinking. It's a bit heavy on the examples and a bit light on the details, but I suppose equations are off-putting and the author may want to keep his methods hidden. That's fine, but it rather undermines the book's title. Still it's enjoyable, intelligent, and good at explaining how to view the world in a probabilistic manner. ( )
  le.vert.galant | Jan 26, 2015 |
Very enjoyable read with some fabulous chapters. I wanted to read it for the Pecota chapters but I loved most many of the politics, climate and health chapters even more. ( )
  lincolnpan | Dec 31, 2014 |
Nate Silver, catapulted to fame by some lucky political predictions a few years ago, on appropriate uses for data, models, and forecasting. Although I believe his fame relative to peers is based in some significant part on luck, he knows what he's talking about. This book is a good primer in Bayes theorem and uncertainty, how they apply to predictions, and a variety of domains in which prediction is used. It's a bit repetitious, but not so much so as much non-fiction.

I particularly liked the directness of this book in explicating what is straightforward and what is difficult to predict, and the reasons why. There is a lot of good in here for people at high levels in an organization. For people actually doing the work, I'm less enamored -- perhaps because it all seems like the sort of thing that they should know already.

The deliberate misrepresentation of data to result in more advantageous psychological responses (as per the Weather Channel suggesting a much higher chance of a negative outcome than a positive one, akin to losses/gains in social psych) stood out among the anecdotes. Part of me wishes there had been a bit more discussion of appropriate and inappropriate circumstances for truth telling vs. massaged truth telling, because that seems like a deep, useful and interesting subject. It's not quite in the target zone of this book, though. ( )
  pammab | Nov 28, 2014 |
After finishing this book, I found myself charmed by it and liking it a great deal more than I expected. Silver does not promote himself as a great predictor, but he does describe what his method is. He makes a strong argument for using his favored methods for making predictions (primarily using Bayes' Theorem).

Silver does take a number of pot-shots at television pundits, however, since the book was written before the 2012 election and considering the ass-whupping Silver and his data-crunching colleagues delivered to the Corps of Punditry (tm), I'll have to invoke the Ali rule and declare: "It ain't bragging if it's true." Silver makes a very convincing argument that a forecaster using sound techniques and with access to adequate data sources will out-forecast any number of experienced experts using only their gut.

In the end, however, Silver does not set up Bayesian forecasting as a replacement for expertise and personal wisdom. His goal is more accurate predictions, not winning a fight or a popularity contest. I believe Silver's wisdom comes from his background at making predictions. Both in baseball and in poker, the forecaster is wrong more often then right. Silver's approach is not to over-inflate his own reliability, rather to accurately represent how accurate his predictions are and to honestly work to make them more accurate in the future.

Implicitly then, this is a book about rhetoric (or how to make arguments with forecasts) as it is about the act of forecasting itself.

It is curious that for a book about crunching data (and big data) Silver makes his arguments with words rather than with numbers. Readers wanting to get their hands on some Bayesian forecasting will have to look elsewhere for the math.

Overall: this is a very good book and I can recommend it to almost anyone. ( )
  nnschiller | Sep 18, 2014 |
Showing 1-5 of 42 (next | show all)
The first thing to note about The Signal and the Noise is that it is modest – not lacking in confidence or pointlessly self-effacing, but calm and honest about the limits to what the author or anyone else can know about what is going to happen next. Across a wide range of subjects about which people make professional predictions – the housing market, the stock market, elections, baseball, the weather, earthquakes, terrorist attacks – Silver argues for a sharper recognition of "the difference between what we know and what we think we know" and recommends a strategy for closing the gap.
added by eereed | editGuardian, Ruth Scurr (Nov 9, 2012)
What Silver is doing here is playing the role of public statistician — bringing simple but powerful empirical methods to bear on a controversial policy question, and making the results accessible to anyone with a high-school level of numeracy. The exercise is 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. Except that their authority was based to varying degrees on their establishment credentials, whereas Silver’s derives from his data savvy in the age of the stats nerd.
added by eereed | editNew York Times, Noam Scheiber (Nov 2, 2012)
A friend who was a pioneer in the computer games business used to marvel at how her company handled its projections of costs and revenue. “We performed exhaustive calculations, analyses and revisions,” she would tell me. “And we somehow always ended with numbers that justified our hiring the people and producing the games we had wanted to all along.” Those forecasts rarely proved accurate, but as long as the games were reasonably profitable, she said, you’d keep your job and get to create more unfounded projections for the next endeavor.......
added by marq | editNew York Times, LEONARD MLODINOW (Oct 23, 2012)
In the course of this entertaining popularization of a subject that scares many people off, the signal of Silver’s own thesis tends to get a bit lost in the noise of storytelling. The asides and digressions are sometimes delightful, as in a chapter about the author’s brief adventures as a professional poker player, and sometimes annoying, as in some half-baked musings on the politics of climate change. But they distract from Silver’s core point: For all that modern technology has enhanced our computational abilities, there are still an awful lot of ways for predictions to go wrong thanks to bad incentives and bad methods.
added by eereed | editSlate, Matthew Yglesias (Oct 5, 2012)
Mr. Silver reminds us that we live in an era of "Big Data," with "2.5 quintillion bytes" generated each day. But he strongly disagrees with the view that the sheer volume of data will make predicting easier. "Numbers don't speak for themselves," he notes. In fact, we imbue numbers with meaning, depending on our approach. We often find patterns that are simply random noise, and many of our predictions fail: "Unless we become aware of the biases we introduce, the returns to additional information may be minimal—or diminishing." The trick is to extract the correct signal from the noisy data. "The signal is the truth," Mr. Silver writes. "The noise is the distraction."

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Amazon.com Amazon.com Review (ISBN 159420411X, Hardcover)

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

(retrieved from Amazon Mon, 30 Sep 2013 13:38:29 -0400)

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. Drawing on his own groundbreaking work, Silver examines the world of prediction.

(summary from another edition)

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Penguin Australia

2 editions of this book were published by Penguin Australia.

Editions: 0141975652, 1846147735

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