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

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

3.95 of 5 stars 3.95 · rating details · 5,238 ratings · 780 reviews

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 FiveThirtyEight.com, where Silver is one of the nation’s most influential political forecasters.

Drawing on his own groundbreaking work, S...more 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 FiveThirtyEight.com, 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.(less)

Hardcover, 544 pages

Published September 27th 2012 by Penguin

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Jan 01, 2013 Charles rated it 4 of 5 stars

The Signal and the Noise is a very interesting book with mixed success: 3 1/2 stars, were this permitted. I found it somewhat difficult to review; however, my entire book group – without exception – had similar opinions. I would encourage you to view this as a group opinion.

At its best, TSANTN is interesting, illustrative, educational, and provocative. And many chapters – including banking, the weather, volcanoes, elections, and poker – were exactly that. Four stars, without hesitation. The prob...more The Signal and the Noise is a very interesting book with mixed success: 3 1/2 stars, were this permitted. I found it somewhat difficult to review; however, my entire book group – without exception – had similar opinions. I would encourage you to view this as a group opinion.

At its best, TSANTN is interesting, illustrative, educational, and provocative. And many chapters – including banking, the weather, volcanoes, elections, and poker – were exactly that. Four stars, without hesitation. The problem is that some chapters – including baseball, terrorists, and the last several – were dull. Either too long or too scattered or just not interesting. (Again, this was the unanimous opinion among my group.)

Nate Silver is a wunderkind polymath, who has scored resounding successes in statistical applications to baseball, poker, and, most recently and most impressively, politics. He emphasizes that huge bunches of data are the tools needed for predictions and that there are huge bunches of data out there. He calmly points out that some things are predictable and are predicted, using various methods with resultant various success. Some things that are predictable are not predicted accurately, exactly because the wrong tools or approaches are used. He equally argues that some things are not predictable, and when predicted, have, predictably, low success. Poor predictors often share the characteristics of ignorance of facts, inappropriate application of basic probability analyses, and, especially, overconfidence. Forecasts are made more inaccurate by overfitting – confusing noise for signal.

His grasp of applied math and statistics is refreshing. His application – although, perhaps not the explanation - of Bayes theorem is lucid. His writing style is casual, more impressive considering the subject material.

As has been noted by others, the number of typographical errors is unacceptable. An even greater editorial error is letting the author ramble on (again, in some chapters). Liberal use of both a sharp red pencil and an X-Acto knife would have improved this book.

So, overall, I really liked some parts. This is why I gave the book a 4-star review. (Most of my book group ended up awarding only 3-stars). But, overall, after a few strong opening innings, the precision of text and purpose waned. In the beginning I did not want the book to end; by 2/3 of the way through, I was more than ready.

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Stephanie Sun Eric wrote: "My point was simply to illustrate the epistemological difficulties that arise when we give to much weight to observations of any sort......more
Apr 11, 2013 03:02pm

Stephanie Sun Also since the output of Bayes theorem is a probability in percentage form, I don't think you always need some grand sounding conclusion to use data p...more
Apr 11, 2013 03:07pm

May 04, 2013 Stuart rated it 3 of 5 stars

Up front, I should say that I used to work in earthquake prediction and ten years ago I actually proposed writing a broad book like this about prediction and why it usually doesn't work. No publisher wanted it, but then again I'm not Nate Silver. I'm glad someone put together a sane, cool look at the successes (rare) and failures (often) in the prediction game that actually is getting read. There was a very good book on this topic, The Fortune Sellers, that came out about 15 years ago and was ig...more Up front, I should say that I used to work in earthquake prediction and ten years ago I actually proposed writing a broad book like this about prediction and why it usually doesn't work. No publisher wanted it, but then again I'm not Nate Silver. I'm glad someone put together a sane, cool look at the successes (rare) and failures (often) in the prediction game that actually is getting read. There was a very good book on this topic, The Fortune Sellers, that came out about 15 years ago and was ignored. In contrast to that book, The Signal and the Noise has an author with name recognition and is more statistically oriented.

There is a lot to like here in terms of tone. Silver is very cautious about making claims about the efficacy of prediction science. He examines a broad array of applications of statistics to making real world predictions - earthquakes, baseball, bond ratings, elections and more - and stays away from getting deep in the math in order to keep the layman reader interested (I hope that's true at any rate; I don't think people are buying this book and after reading a few pages, letting it accumulate dust.).

That all said, Silver isn't a good writer. The sentences in this book are often flat. Then there is the organization of this book, which is haphazard. Ultimately, this book is about when Bayes theorem can be used in prediction and how it should be used. But this theorem doesn't get mentioned until the middle of the book. Without that central theme up front, The Signal and the Noise is a hodgepodge of applications. Also, a key to most predictive work are Monte Carlo simulations (which are central to Silver's own predictions), yet there is nothing mentioned about Monte Carlo approaches. This book needed an editor with a quantitative bent who could have not only pushed Silver to write with verve, but also molded the assortment of observations into something that had intellectual clarity.

Ultimately, this book is perhaps one heavy edit away from being a classic. In its current form it's a meandering survey of some prominent and obscure applications of prediction science that isn't shy about including pitfalls. If numbers aren't your thing, you'll be bored with this book from the get go. If you're numbers and chart oriented, but aren't a quantitatively oriented scientist who already knows much of this material, you'll probably find the first third to half of this book interesting.(less)

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Mar 26, 2013 Justin rated it 2 of 5 stars

I had read most of this book with a fair degree of equanimity - finding some faults, but also a lot of good information in it. Then I'm jarred out of complacency by a sudden shot from nowhere, in which he says that David Hume, one of the greatest philosophers of the 18th century, is simply too 'daft to understand' probabilistic arguments. Without any introduction to the subject, he claims Hume is stuck in some 'skeptical shell' that prevents him from understanding the simple, elegant solutions o...more I had read most of this book with a fair degree of equanimity - finding some faults, but also a lot of good information in it. Then I'm jarred out of complacency by a sudden shot from nowhere, in which he says that David Hume, one of the greatest philosophers of the 18th century, is simply too 'daft to understand' probabilistic arguments. Without any introduction to the subject, he claims Hume is stuck in some 'skeptical shell' that prevents him from understanding the simple, elegant solutions of Bayes.

What makes this so painful to read is that it shows Silver has never even taken the time to read Hume, at least not more than the two paragraphs he used to cite his sources. If he had even kept on for five more pages he would have found that Hume was defending the very type of probabilistic arguments that Silver said Hume was 'too daft' to understand. Nate seems to have given a cursory glance to a single page of Hume's work - "SCEPTICAL DOUBTS CONCERNING THE OPERATIONS OF THE UNDERSTANDING," without even bothering to proceed to the very next section - "SCEPTICAL SOLUTION OF THESE DOUBTS," in which Hume lays a rational foundation for belief in the absence of certainty.

In fact, the entire 'Enquiry of Human Understanding' can be read as a treatise attempting to supplant abstract and questionable a priori proofs, with more sensible arguments grounded entirely in the test of experience and probability. By brushing Hume aside so casually, Silver spits in the face of his own philosophical progenitor - a man who helped plant the foundations for the sort of thinking that Silver now takes for granted.

I am sure the vast majority of readers will roll a bemused eye at my anger over trivial details like this - but not only does it show that Silver very often doesn't take the time to understand his sources (see Michael Mann's critique of Silver's presentation of global warming), but Silver's casual remarks could easily turn a lot of readers off to Hume before they've even read him. Trendy books like Silvers are far more popular than classic works of philosophy, and new readers are likely to take Silver's description as an accurate portrayal of that daft, old skeptic, David Hume. (less)

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Oct 27, 2012 Michael Austin rated it 5 of 5 stars

Shelves: read-in-2012

Nate Silver has done an incredible (and, quite possibly an unpredictable) thing with _The Signal and the Noise_: He has written an extremely good book when he didn't even have to. Nothing is more common than for someone like Silver--a media phenom with a strong platform (his 538 blog) to phone a book in to cash in on his 15 minutes. I have probably read two dozen books in the past five years that do exactly this. But _The Signal and the Noise_ is a much more substantial book than, say, _The Blac...more Nate Silver has done an incredible (and, quite possibly an unpredictable) thing with _The Signal and the Noise_: He has written an extremely good book when he didn't even have to. Nothing is more common than for someone like Silver--a media phenom with a strong platform (his 538 blog) to phone a book in to cash in on his 15 minutes. I have probably read two dozen books in the past five years that do exactly this. But _The Signal and the Noise_ is a much more substantial book than, say, _The Black Swan_ or either of the _Freakonomics_ offerings. It is a wide-ranging, in-depth look at the ways that we are wired to make predictions (and the reasons that these are so often wrong).

Silver ranges over a variety of prediction environments: baseball, chess, poker, the stock market, politics, weather, and terrorist attacks to name the most interesting. Throughout it all, he reminds us that human beings are pattern-seeking animals and that we are just as likely to build patterns where none exist as we are to find the correct patterns and harness their predictive capacity. Predictions work best when they are 1) probabilistic (i.e., express a range of possibilities and assign probabilities for each); 2) when they use as much information--both statistical and analytical--as possible; and 3) when they are continually revised to account for new information.

As logical as these sound, human nature seems to drive us in three opposite directions: 1) we seek predictions that are definite and can be acted upon (i.e. "Obama will beat Romney," or "it will rain tomorrow"); 2) we gravitate towards methodologies that seem to discover a magic bullet formula that guarantees success; and 3) we feel compelled to stand by our predictions even as they become increasingly unlikely. Seasoned prognosticators play a long game. Under the right circumstances (a poker game, for example), a strategy that produces only a sightly better prediction than random chance can produce huge dividends.

Perhaps most surprisingly, Silver is a great writer (or, at least a great explainer). As an English major with very little grounding in statistics, I could still understand everything he said. Even more importantly, his narratives are interesting. Who could have predicted that from America's most famous stat-geek? (less)

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Feb 02, 2013 Cameron rated it 5 of 5 stars · review of another edition

This is a really amazing book - a must read for anyone who makes decisions or judgement calls. Even before I had finished the book it caused me to look at some of the assumptions and bad forecasts I was making as well as recognising "patterns" as noise.

There is nothing "new" in this book, just well established and solid methods applied well and explained very coherently. The writing is excellent, the graphics helpful and the type not too small. There are plenty of footnotes (relevant to the page...more

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Apr 23, 2013 Mike Mueller rated it 4 of 5 stars · review of another edition

I followed Nate Silver's blog (FiveThirtyEight) closely during the run-up to election day 2012. His premise was simple: grab every public poll possible, attempt to correct for pollsters' known biases, and produce a forecast based on the result. Somehow no one had thought to do this before. Silver simply crunched the numbers and nailed the outcomes in every state. Meanwhile, pundits, bloggers, and assorted blowhards made predictions based on nothing but gut feeling and partisan hackery, and they...more I followed Nate Silver's blog (FiveThirtyEight) closely during the run-up to election day 2012. His premise was simple: grab every public poll possible, attempt to correct for pollsters' known biases, and produce a forecast based on the result. Somehow no one had thought to do this before. Silver simply crunched the numbers and nailed the outcomes in every state. Meanwhile, pundits, bloggers, and assorted blowhards made predictions based on nothing but gut feeling and partisan hackery, and they mostly missed the mark (often by a wide margin).

I was looking forward to reading more about his methodology in this book, as well as his take on the principles involved in making predictions from noisy data. In this regard, I wasn't disappointed. Silver does a good job of laying out the rules of the road:

* It's easy to mistake essentially random fluctuations for a meaningful pattern, and in some contexts (say, earthquake predictions), this can have devastating results.
* Having a well-formed, testable theory is better than just looking for any correlations you can find in your data set.
* Always make predictions and update your probability estimates like a good Bayesian. Your predictions should approach reality as you continually refine them.
* Watch out for biases in yourself and in your data set.
* Often overlooked: make sure incentives are aligned with the results you would like to achieve.

Also, some specific interesting facts:

* Making a living at poker is really hard. Without any really bad players at the table, it's nearly impossible for anyone but the top players to turn a profit.
* The efficient market hypothesis doesn't hold up to scrutiny; however, even though the stock market has discernible patterns, it may not be possible to exploit the patterns and consistently beat the market.
* Weather prediction has gotten a lot better in the last couple decades, even though most people think it hasn't.
* Both earthquakes and terrorist attacks follow a power law distribution.

If you're a stock trader, scientist, gambler, or simply someone who wants to form an accurate picture in a noisy environment, there's something in this book for you. It's nice to see this kind of clear-headed, rational thinking becoming sexier recently. The book is also well cited, which helps give weight to some of the more counterintuitive claims.

There was a missed opportunity to spend some time on results from the medical research industry. It's well known that publication bias and other factors result in misleadingly positive results for new treatments, which ultimately go away after independent researchers attempt (unsuccessfully) to reproduce the results. It seems like a pertinent, prototypical case of finding patterns in noise, one which could have been instructive.

A final note: Silver is not the best writer; his prose is uneven and occasionally downright awkward. His casual style works fine for a blog, but here it diminishes the impact the book could otherwise have had. This is his first published book, and it shows. There are also a couple glaring mistakes that make me think he needed a better editor.
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Jan 31, 2013 Gerald rated it 3 of 5 stars · review of another edition

I really enjoyed this - it was actually what I expected, but I'm not sure if its what everyone expects.

Firstly, don't bother unless you're a complete statistics geek (I am). But of statistics, there's loads and loads, from baseball to poker to chess to 9/11 (and terrorism) to weather to earthquakes. So many great graphs and use of statistics tests to prove or disprove something.

Mostly its about Bayesian theory - how and when its used, along with what it could be used for.

What it's not is a Nate...more

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May 11, 2013 David Leroy rated it 4 of 5 stars

The Signal and the Noise is no light read, however, it is deeply fascinating. In the course of thirteen chapters with a conclusion, he moves through the world of “Fat Tail” events, and a thick dark historical flock of “Black Swans,” (a term for an unforeseen outcome) as he delivers the details of the story behind various predictions gone horribly wrong. In an age of computers, armed with advanced statistical programs, one would expect that man has come to the point of eliminating at least some o...more The Signal and the Noise is no light read, however, it is deeply fascinating. In the course of thirteen chapters with a conclusion, he moves through the world of “Fat Tail” events, and a thick dark historical flock of “Black Swans,” (a term for an unforeseen outcome) as he delivers the details of the story behind various predictions gone horribly wrong. In an age of computers, armed with advanced statistical programs, one would expect that man has come to the point of eliminating at least some of the unknown of the future. However, Nate Silver systematically demonstrates to the reader that we are less skilled at predictions than we would like to admit.

Signal stands for truth, and the Noise represents all of the competing untruths within a data set. The task of prediction is extracting that Signal from the Noise. The vast majority of predictions fail due to a simple principle which I became familiar with in college as I suffered through a logic class. Correlations do not logically imply causation. However, for the person who is trying to make a prediction regarding markets, the weather, or even baseball, the temptation to mistake a correlation as a Signal leading to a accurate prediction is too much to resist.

Among the more interesting accounts is the story of basketball professional gambler Haralobas “Bob” Voulgaris. He makes his predictions as he beholds five games simultaneously broadcasted across a series of five large flat screen T.V. sets in his ultra modern home. Voulgaris makes between one million in an off year, to six or seven million in a good year. What is his secret system? He has none. Voulgaris hedges his bets, and takes in every statistic regarding Basketball he can get his hands on, however, he does not rely upon a single system, but uses his own mind to process and make predictions based upon critical thinking skills.

A GPS can get you in more trouble than taking a wrong turn or simply giving you inaccurate directions. The worse thing a GPS can do is land you dead center into a traffic jam created by its own program to avoid such slow downs. This is called a self-canceling prediction. Drivers in major cities like London, using a GPS system for navigation will be directed to take one route over another based upon some traffic slow down on a competing route. What happens when all the GPS users are directed to the alternate route? Apparently it can result in the creation of a slow down even worse than the one the driver hoped to avoid.

The potential reader should keep in mind that this book is not an easy magazine article. Drifting in and out of “The Signal and the Noise” before bedtime would produce some difficult dreams. I highly recommend this book to the “Data Monsters” among us, because I think they will find fascinating the charts, graphs and numerous tables throughout the text. “This is a book about information, technology and scientific progress,” Nate Silver opens in the introduction. He is right. Every business attempts to predict the future, therefore, I believe there is something in his work for every business, including AT&T.
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May 09, 2013 Benjamin Ferdinand rated it 3 of 5 stars

Forecasting future outcomes is required in almost every human activity: from leaving the apartment with an umbrella to folding in a game of “Texas Hold-em.” In The Signal and the Noise, author Nate Silver uses areas of relative expertise (e.g. Poker and Baseball), as well as less familiar topics (e.g. Earthquake forecasting, Virus outbreaks), to highlight common biases that tend to skew the accuracy of predictions. Silver strolls through each chapter-topic in an accessible manner, punctuating hi...more Forecasting future outcomes is required in almost every human activity: from leaving the apartment with an umbrella to folding in a game of “Texas Hold-em.” In The Signal and the Noise, author Nate Silver uses areas of relative expertise (e.g. Poker and Baseball), as well as less familiar topics (e.g. Earthquake forecasting, Virus outbreaks), to highlight common biases that tend to skew the accuracy of predictions. Silver strolls through each chapter-topic in an accessible manner, punctuating his text with compelling charts of power-law distributions and Pareto efficiencies, to create a piece that is both entertaining and informative.

While the book’s subject matter is diverse, the author’s theoretical approach to forecasting is more uniform. Silver advocates for the broader use of Bayesian principles. Put simply, such a framework would allow us to update our beliefs about the world based on what we observe. Regularly updating prior probability estimates with subsequent observations allows us, in the words of Thomas Bayes, “to learn about [the universe] through approximation, getting closer and closer to the truth as we gather more evidence."

Never venturing too far into the esoteric, “The Signal” is at its best when analyzing real-world phenomena through the lens of an arm-chair statistician, imploring us to apply more rigor when evaluating predictions in fields where noise is often mistaken for a signal. The chapters are fairly modular, so one has the ability to pick and choose topics without reading prior chapters for context. I was most impressed by the chapter that focuses on Earthquakes forecasting, as well as the chapter focusing on poker.

While each chapter represents somewhat of an “instalment,” their interchangeable nature may also highlight the book’s most glaring fault in my eyes - there is not much coherence to the thing. It seems that Silver took full advantage of the non-blog format to examine anything and everything he found interesting, but he could have done a better job paring down the word count a little bit. I guess one couldn’t sell the hardcover for $25 dollars a piece in that case.

Regardless, the true value of Nate Silvers book lies in its wide-scale applicability. With so much of our day-to-day life driven by rudimentary predictions, we could all benefit from a deeper understanding of probability and uncertainty. The author maintains that as our appreciation for such uncertainty improves, our predictions can get better too…..for a more fulsome explanation of this uncertainty, I would wait for the paperback edition or just go to his blog.(less)

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May 04, 2013 Matt Asher rated it 3 of 5 stars

Some good insights here, as well as a very good chapter on IBM's chess playing computer. Most mainstream accounts spin this narrative as "computer finally vanquishes man at chess." The real, full story is much more complicated and shows just how much the competition between Kasparov and Deep Blue was really between one man and a team of programmers who made continual adjustments to their algorithm. The section on rating a shortstops fielding abilities was equally excellent.

On the downside, Silve...more

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May 02, 2013 Kevin rated it 4 of 5 stars

I would give this about 3.5 stars. I read this book for a book club at work. Whether it was a good choice for that is another story, but overall, I was surprised by how much I liked this book. I thought it was going to be boring, irritating, partisan, pseudo-science used to try to bolster positions on topics that I didn’t really agree with. He tends to have rather agnostic positions about the topics he discusses, which makes the book decent. Books like this tend to seem to be one of two flavors:...more I would give this about 3.5 stars. I read this book for a book club at work. Whether it was a good choice for that is another story, but overall, I was surprised by how much I liked this book. I thought it was going to be boring, irritating, partisan, pseudo-science used to try to bolster positions on topics that I didn’t really agree with. He tends to have rather agnostic positions about the topics he discusses, which makes the book decent. Books like this tend to seem to be one of two flavors: journalists like Malcolm Gladwell (the Tipping Point) who are good at presenting information and have books that aggregate a bunch of research and anecdotes into a very accessible book and researchers like Steven Levitt (Freakonomics) who write about their own research and tie in some related topics. Nate Silver is not an academic doing rigorous research like Levitt, but thinks he is knowledgeable enough to deduce whether ideas are good or bad or strong or weak. His background is in internet poker, baseball statistics, and aggregating political election polls. These do require some intimacy with math, but nothing that most math majors (provided they have some background in probability and statistics) wouldn’t have a great deal of familiarity with.
I did like a number of parts of the book. Many of the things it talked about or highlighted are topics that I’ve either seen before or are not terribly unobvious, but the book had some interesting stories and provided good reminders of those ideas. Some of the ideas that were most useful include: being clear what your assumptions are and distinguishing between facts and assumptions, the need for being clear with the level of uncertainty in a prediction (the river will only rise 49 feet, just short of the 51 foot levee, but the margin of error is 7 feet!)(178), overfitting can make a model look better but perform worse (167), the importance of not being married to your initial idea if better ideas come along (the notion of a person who is a hedgehog about ideas versus a fox) (53), the importance of updating models with new information to calibrate them (244), distinguishing whether a perceived anomaly is a feature or a bug (285), how sophisticated methods often cannot compensate for bad data – garbage in, garbage out (289), agreement between forecasters is not related to accuracy (382), confusing the unfamiliar with the improbable (419), and many more. There were a few topics that he beat to death that I could have had a lighter dose of. The first thing that comes to mind is earthquakes. They’re hard to predict, and more data has not made us more accurate at predicting them. He basically repeats that over and over for 33 pages. I wasn’t that enamored with the chapters that talked about his own exploits in poker and baseball statistics. It’s what he knows, but they didn’t seem as big and important as many of the other topics covered. He also starts talking about Bayes’ theorem about halfway through the book and goes on and on about it at every turn. I felt like it could have been more tactfully integrated.
Overall it’s a very readable book that honestly seems to be seeking the truth, and keep you thinking about ways that you are either deceiving yourself or others regarding your estimates of what is going on. It’s not a very technical book, but it has some good content that people who deal with data in business can probably use to help make better and more honest decisions. It’s a little long for how much content it has, but it’s relatively worth it.

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Apr 29, 2013 Brick rated it 4 of 5 stars

An excellent read, with very detailed insights into areas in which Silver has a wealth of personal experience, including limit Texas Hold'em, baseball statistics used in forecasting player performance, and political polling, combined with insights derived from extensive interviews with top practitioners in many, many other diverse areas. Although I worked my way through more details on professional gambling than I would have thought I could handle, similarly baseball performance forecasting, the...more

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Apr 28, 2013 Jafar rated it 3 of 5 stars

This is a good book on the role of probability in natural sciences, social sciences, games, and sports. Silver examines areas where we've done poorly in our predictions (e.g. economy, earthquakes, infectious diseases, global climate change), where we've made improvements in our predictions (e.g. weather forecast), and where we can do fairly well due to the availability of good data (e.g. sports, games, elections). The chapter on how Deep Blue beat Kasparov is fascinating, but a single paragraph...more

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Apr 21, 2013 Devyn Duffy rated it 4 of 5 stars Recommends it for: Anyone interested in forecasting

Pretty good survey of how predictions go wrong, how they sometimes go right, and what can be done to improve forecasting in subjects as diverse as terrorism and earthquakes. Engaging and filled with numbers as well as with personal stories from Silver's interests in baseball, poker, and electoral forecasting.

Interestingly, although Silver supports what he calls a Bayesian approach to forecasting, and he shows that a Bayesian approach is a sound one, he also manages to falsify Bayes's theorem its...more

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Apr 21, 2013 Laura rated it 3 of 5 stars

Recommended to Laura by: NPR

Shelves: being-human, economics, history

A love song to Bayesian reasoning on par with http://xkcd.com/1132/.

Proposes an answer to a question that baffled me during the most recent economic collapse; why so many people driving the crisis maintained, with all apparent earnestness, that no one saw it coming while the record reveals that LOTS of people did. Lots of people spoke movingly about housing bubbles. Lots of people suspected that collateralized debt obligations were far riskier than rated and some were vastly overvalued. But the...more

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Apr 18, 2013 David rated it 5 of 5 stars

Shelves: economics, environment, mathematics, meteorology, science, geology

This is a fantastic book about predictions. I enjoyed every page. The book is filled to the brim with diagrams and charts that help get the points across. The book is divided into two parts. The first part is an examination of all the ways that predictions go wrong. The second part is about how applying Bayes Theorem can make predictions go right.

The book focuses on predictions in a wide variety of topics; economics, the stock market, politics, baseball, basketball, weather, climate, earthquakes...more This is a fantastic book about predictions. I enjoyed every page. The book is filled to the brim with diagrams and charts that help get the points across. The book is divided into two parts. The first part is an examination of all the ways that predictions go wrong. The second part is about how applying Bayes Theorem can make predictions go right.

The book focuses on predictions in a wide variety of topics; economics, the stock market, politics, baseball, basketball, weather, climate, earthquakes, chess, epidemics, poker, and terrorism! Each topic is covered lucidly, in sufficient detail, so that the reader gets a good grasp of the problems and issues for predictions.

There are so many fascinating insights, I can only try to convey a few. At the present time, it is impossible to predict earthquakes, that is, to state ahead of time when and where a certain magnitude earthquake will occur. But it is possible to forecast earthquakes in a probabilistic sense, using a power law. Likewise, it may be possible to forecast terrorism, because that too, follows a power law! (Well, it follows a power law in NATO countries, probably because of the efforts to combat terrorists. But in Israel, the tail of the curve falls below the power law, likely because of the stronger anti-terror emphasis there.)

The accuracy of weather predictions increases slowly but steadily, year by year. Ensembles of computer model runs are part of the story, but human judgment add value, and increases the accuracy. Weather forecasts issued by the National Weather Service are unbiased in a probabilistic sense. But weather forecasts by the TV weatherman are very strongly biased--the weatherman over-predicts precipitation by a significant amount.

Nate Silver shows that the people who are most confident are the ones that make the worst predictions. The best predictions are those that are couched in quantitative uncertainties. Silver shows how Bayes Theorem can be applied to improve predictions; it is all about probabilities. And I just love this footnote,

A conspiracy theory might be thought of as the laziest form of signal analysis. As the Harvard professor H.L. "Skip" Gates says, "Conspiracy theories are an irresistible labor-saving device in the face of complexity."

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Apr 18, 2013 Susan rated it 4 of 5 stars

I read this because I heard Silver on the radio before the election talking about how wrong so many electoral polls are and how right his was in 2008. And he predicted that Obama would win, which I found comforting because the news was not very comforting.

The book discusses prediction, not only in terms of what it takes to have good predictions, but in terms of possibility of prediction. 100 years ago weather prediction was all but impossible; how they're pretty good. And hurricane prediction h...more

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Apr 11, 2013 Yu rated it 4 of 5 stars

Nate Silver writes in a very easy, conversational style. It's almost like sitting next to the man and listening to what he thinks about statistics. That said, the book delves into the details of a wide range of subjects to which accurate predictions are essential, and talks about the arbitrariness of the significance levels we use today. He mainly discusses the importance of it to seismology, baseball, weather reporting, poker games and terrorism. There is also a great section in which he talks...more

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Apr 05, 2013 Nick Chandler-klein rated it 3 of 5 stars

Shelves: read-in-2013

A very good book on its stated topic - the realities of forecasting.

The book suffers greatly when he strays from that particular topic. Very simplistic dismissals of detailed and complex arguments from top-notch intellectuals.

He also has an extremely strong and often-stated opinion on the Bayesian vs. Frequentist statistics argument, which would be fine if not for the fact that everything he writes about it suggests that he does not really understand either side of the argument. He assigns a lo...more

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Mar 25, 2013 Ed rated it 5 of 5 stars

I loved this book. It is one of the most interesting and intelligent books I have read for years. And I found his writing style very comprehensible. Indeed his explanation of Bayes Theorem which has eluded me for years finally made the penny drop. His chapters cover not only political and economic forecasting, but weather, climate, sports, earthquakes and many other areas. His examples are brilliantly illustrative and he is rightly cautious on the whole area of forecasting, risk and uncertainty....more

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Mar 24, 2013 Oscar rated it 4 of 5 stars

It's interesting in how he applies Bayesian Probability Theory in varying contexts. However, I ultimately found it unfulfilling. Maybe I can get more out of it in a second reading but I was looking for a more detailed, how-to book as well as the some of the underpinnings. Instead I mostly found it superficial in how to apply it. He makes a semi-convincing argument of the advantages, most notably through his incredible success in baseball and elections, however what about the underpinnings and as...more

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Mar 15, 2013 Vincent Russo rated it 4 of 5 stars

This book covers various statistical misconceptions in variety of settings including but not limited to poker, weather prediction, baseball, terrorist attack prediction, and more. Its main thesis is that in order for one to produce accurate and reliable statistical models, it’s essential to differentiate between the signal (the actual data) and the noise (the surrounding misleading or perhaps uncorrelated data). In each of these fields a number of interesting anecdotes are presented to illustrat...more

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Feb 24, 2013 Shane rated it 5 of 5 stars

This book has entered the select group of books that I consider essential reading for those who want to learn something substantial when they read. For those with some background in probability and statistics (electrical engineers and mathematicians), the basics in the book may be straightforward, but there will be tidbits of new information for you. For the layperson, a little patience and thought will hopefully open your eyes to the power of probability and statistics. Examples in the book ran...more

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Feb 23, 2013 rmn rated it 3 of 5 stars

Shelves: financial-math-non-fiction, non-fiction-other, non-fiction-science

Highly recommended for anyone who believes anything "pundits" on tv say or anyone who doesn't look beyond the media's packaged narratives of the world. Unfortunately, those who need to read this book the most are exactly the ones who won't. And while there are hundreds of books out there like this pointing to the misuses of data and overconfidence in forecasters' own abilities to forecast, this is one of the most accessible.

Silver's main points are:

1. We now have a lot of data
2. That data can be...more

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Feb 20, 2013 El_kiablo rated it 3 of 5 stars

Nate Silver is clearly trying to do the "unusual analysis of normal occurances" thing that Freakonomics did, although his topics are a bit bigger and his discussion is a little more numbers oriented. Unfortunately, it's also less engaging. He's not a terrible writer, per se, especially given how data driven he is, but he's also not particularly compelling.

A lot of the subjects he covers are potentially interesting. The problem is that the academic tone combined with his often abstract subject m...more

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Feb 14, 2013 Georgia rated it 3 of 5 stars

I think this book would have been better if it had stuck to the analytical journalistic style of the first few chapters. Silver does a good job of describing the problems and progress of various predictive fields, including weather forecasting, economic forecasting, earthquake detection and election analysis. The chapters about poker and the betting industry seemed to be the main vehicle for much of Silver's argument, and were a little overextended. Although there's nothing particularly original...more

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Feb 13, 2013 Jorge rated it 4 of 5 stars

Nate Silver has become quite famous by using statistical tools to make predictions, from baseball to his success in forecasting the last presidential elections. In the midst of so much ‘fog of election’s war’, he was one of the few sane voices that relied on fair analysis of pooling data to predict results in every state. That we have these days so much science but also so much ‘noise’ is the genesis of his book.

Just the first few pages of the book, comparing our era of over-information with the...more

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Feb 01, 2013 David rated it 4 of 5 stars

Shelves: nonfiction, pop-science, mathematical-me

Data science is the buzzword in the eyes of funding agencies, hiring committees, and technically-oriented press alike. CS departments are desperate to get people who do "big data," and statisticians and scientists who actually have experience with data management are suddenly hot commodities. NSF explicitly calls for proposals that address "big data" problems. And it's not just a buzzy topic among technical folks. Listen for the stories on predicting flu outbreaks based on Google search patterns...more Data science is the buzzword in the eyes of funding agencies, hiring committees, and technically-oriented press alike. CS departments are desperate to get people who do "big data," and statisticians and scientists who actually have experience with data management are suddenly hot commodities. NSF explicitly calls for proposals that address "big data" problems. And it's not just a buzzy topic among technical folks. Listen for the stories on predicting flu outbreaks based on Google search patterns. Look for the stories about how companies greedily buy up personal information in order to make ever more finely-tuned ad campaigns. Even reading general news during the last election, I would from time to time run into something about the Obama campaign running A/B tests on their web site.

And then, of course, there was Nate Silver and FiveTwentyEight.

Now, elections are special. We've been running them for a long time, and we've kept records. I was not particularly surprised when Silver's predictions were accurate. What was special about Silver was not necessarily his model, but the fact that he was there to give a clear voice to the notion that the results could be predicted by a simple model driven from polling data. The great thing about this book is that Silver manages to keep that same clear voice -- popularizing without dumbing down -- in presenting a reasoned view of prediction more generally. The problems of noise, overfitting, and data presentation are neatly mixed in with stories both from Silver's own career and from the careers of professional predictors from many other walks of life, from geologists and meteorologists to economists to professional gamblers.

The cynic in me thinks that the Obama election has an awful lot to do with the popularity of what is. But I'll take what I can get. In an ideal world, Nate Silver's book would be widely read and understood, and it would convince a lot of people to be more intelligent consumers of predictions. And in the world we have, I'm at least glad to see it making a splash.
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Jan 26, 2013 Julia rated it 4 of 5 stars

First question when I picked up this book was Who is Nate Silver? And why would I want to read a book of his?

The author wastes no time in stamping his authority in the opening pages. Nate Silver is the author of the FiveThirtyEight blog, syndicated by The New York Times, which correctly predicted the results of the 2012 U.S. presidential elections for all 50 states and 49 out of 50 states in the preceding 2008 election. His clear predictions flew in the face of mainstream pundits who hyped up bo...more First question when I picked up this book was Who is Nate Silver? And why would I want to read a book of his?

The author wastes no time in stamping his authority in the opening pages. Nate Silver is the author of the FiveThirtyEight blog, syndicated by The New York Times, which correctly predicted the results of the 2012 U.S. presidential elections for all 50 states and 49 out of 50 states in the preceding 2008 election. His clear predictions flew in the face of mainstream pundits who hyped up both elections to be "too close to call" for several swing states.

Prior to FiveThirtyEight, Silver wrote PECOTA, a statistical program which makes projections on individual player performances in major league baseball. Silver also used his statistical skills to make extra income playing online poker.

The non academic background of the author for me lends credence to a book about smarter and more conservative statistical predictions.

It is well researched - Silver interviewed personalities such as Donald Rumsfeld, Tim Bernard Lee and a host of other subject matter experts in their fields covered in the book. This book is about how to make better predictions and separating "noise" from the "signal" in real life data.

Areas covered ranges from the prediction of the 2008 sub-prime mortgage defaults, baseball, poker, global warming, earthquakes, chess and terrorist attacks.

Silver posits that foxes, that is, those who are more cautious with a lower disinclination to challenge their own hypotheses make better predictions than hedgehogs, meaning those who make bold sweeping predictions stemming from deeply entrenched prior beliefs.

The espousal of Bayesian statistics is a recurring theme in this book.

Overall, this book offers fresh perspectives on the art and science of predictions and the diversity of topics covered makes for an interesting read. However, some parts are rather contrived when it comes to seemingly random events, such as predicting occurrences of major terrorist attacks.

I hope to see Silver revealing some of his trade secrets on political polling in his future books.
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Jan 19, 2013 Dave rated it 3 of 5 stars

Silver's gone 99 for 100 on predicting the state winners of the last two presidential elections. Here he goes something like 7 for 13, very good in parts, solid in some, and misfires in others. It's well-researched, mostly objective (but by no means totally), but it rarely covers anything I didn't already know. If you've read Michael Lewis's The Big Short and Moneyball you can skip chapters 1 and 3 and if you've ever had a class that proves pundits are not any more accurate forecasters than the...more Silver's gone 99 for 100 on predicting the state winners of the last two presidential elections. Here he goes something like 7 for 13, very good in parts, solid in some, and misfires in others. It's well-researched, mostly objective (but by no means totally), but it rarely covers anything I didn't already know. If you've read Michael Lewis's The Big Short and Moneyball you can skip chapters 1 and 3 and if you've ever had a class that proves pundits are not any more accurate forecasters than the population at large you can skip chapter 2. In addition, Silver loses his way with the climate change chapter as subjectivity overcomes math and the piece covering his online poker career in lifeless, as I expect it would be for anyone who's not a fan of the game.

Silver's at his best covering the weather (temperature predictions and hurricane landfall site predictions have decreased their margin of error by significant margins in the last few decades; trust the National Weather Service and not your local newscaster for the most accurate forecast), earthquakes (impossible to predict), and the Bayes theorem, which he champions as the best model by which to life your life and conduct your business.

As we learn that it's nearly impossible to beat the stock market over the long run without the benefit of inside information, it becomes clear that the best thing a reader with sound statistical analysis ability can take away from this book, other than making the Bayes theorem a default operating method, is to take that skill and apply it where the analysis to this point is weak. The stock market, baseball, poker - they've been covered, but if you can separate the signal from the noise as the availability of big data overwhelms our ability to parse the useful pieces from it then you can gain a competitive edge in your industry. It's good advice and there are some solid parts of the book, but for such a successful guy there was not much groundbreaking material here. If I weren't a completist I would have read only the chapters that started going somewhere in the first few pages, as the correlation between the first five pages was .92. The exception is the chapter on chess, which was fast out the gate, but faded down the stretch, especially as Silver ignored the fact that Kasparov's loss to Deep Blue was in part triggered by the unfairness of the latter's team getting to see the former's recent matches, but not the other way around. So,yes, Silver's political forecasting is exceedingly accurate and his writing is hit or miss.(less)

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“Most of you will have heard the maxim "correlation does not imply causation." Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don't light a patch of the Montana brush on fire when you buy a pint of Haagan-Dazs.” — 3 people liked it

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