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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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1,133417,229 (3.98)21
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

Work details

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

  1. 10
    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 39 (next | show all)
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 |
I love data. I thought I should just get that geeky admission out of the way since my love of this book is largely based on my love of data and the cool things we can do with it. Nate Silver is an awesome statistician best known for his model that has done a great job predicting election winners. In this book, he looks at a lot of incredibly interesting topics from public issues to sports and policy decisions to natural disasters while analyzing the common mistakes people make when making predictions about the future.

I enjoyed this book so much I almost don't know where to start. It's on a topic I love; using machine learning to make predictions or classifications has always fascinated me. It's just so cool that we can get computers to make decisions too complex for us to make ourselves! And Nate Silver does an amazing job explaining this fascinating topic in an interesting and accessible way without talking down to the reader. He's clearly passionate about the topic. He draws on a rich array of sources to make his points, not shying away from the use of large words or history lessons. And the examples are relevant to everyone (especially everyone in the US, but worldwide too). Combined with the graphs and the analogies, the examples make some pretty complex topics much easier to understand.

I loved how information rich the book was. I learned about many different topics and couldn't stop scribbling down fun facts to use at the beginning of this review. The pop culture references and allusions to the author's own life also made the book more engaging and made reading the book feel like having a casual but intelligent conversation with a friend. Obviously I'm a little biased in my opinion because of my prior interest in this topic, but I think anyone who loves non-fiction should definitely pick up this book.

This review first published on Doing Dewey. ( )
  DoingDewey | Jun 29, 2014 |
The Signal and the Noise was a really great read. It's one of those books that lets you annoy your friends by tediously repeating facts, many of which they already have picked up from reading the book, reading reviews, or other tedious friends. Like when the Weather Channel says it is a 30% chance of rain it rains 30% of the time, but that when it says a 10% chance of rain it really means a 3% chance of rain (they would rather people be pleasantly surprised). Or that earthquakes are unpredictable. Or that minor league baseballs players are difficult to predict and give a good advantage to very good scouts, while in the majors it is different. Or that a particularly great professional sports better will win 56 percent of the time. Or other examples drawn from the areas Nate focuses on: political predictions, elections, the macroeconomy, financial markets, epidemics, earthquakes, terrorism, baseball, chess, and poker.

There is a deeper and more important set of lessons in the book to anyone that has not been sufficiently exposed to Bayesian methods. None of that was new to me, but it is still interesting to read and should be mandatory reading for anyone who has not been exposed to it before.

I take some issue with the presentation of economics. Nate is completely right that macroeconomic forecasting has a terrible track record, and does not even appreciate how terrible its own record is. But he doesn't seem to recognize that there is a lot more to economics than macroeconomic forecasts. And, at least as much of the fault with those forecasts lies with the people demanding and using them as with the people providing them.

And I'm not quite as impressed with Nate's election forecasting--anyone relying just on public polls taken in the days before the election would have correctly picked 49 or 50 of the 50 states in both 2008 and 2012. Nate did not have any magic, but he did have much better perspective on the uncertainty in the forecasts and how to read/interpret the significance of movements well in advance of election day.

But those are quibbles, this book really deserves wide readership, probably starting with all of those who rushed out to buy it after the election and still have it sitting on their shelves. ( )
  nosajeel | Jun 21, 2014 |
Now past the introduction and the first chapter I see that Silver gets 'accurate' and 'precise' wrong, - at least in the way that everyone in data science is using them. His definition of 'risk' and 'uncertainty' seems also unusual and muddled. He cites Brian Owens for a blog entry in Nature, but that is actually written by Asher Mullard in Nature Reviews Drug Discovery and the scientific article behind the fact that Silver cites is Prinz, Schlange, Asadullah. Where is the thoroughness?

Update: I have now completed the book and it was actually quite well in many aspect showing how prediction works and does not work in a broad range of areas. I like the chapters on earthquake and weather where we are told what is difficult to predict (earthquakes) and what we are becoming better and better at (weather). For prediction of the stock market he basically argues that you cannot do better than the index unless you have information that other traders do not have. Here one can note that much modern trading is based on fast machine-based algorithms using information known milliseconds or less before others.

Silver does a good job of introducing climate change prediction, though it would have been interesting to have his opinion on Bjørn Lomborg-type of argument for what interventions we should invoke. ( )
  fnielsen | Apr 28, 2014 |
Showing 1-5 of 39 (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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