The Signal and online the Noise: Why So outlet online sale Many Predictions Fail--but Some Don't online

The Signal and online the Noise: Why So outlet online sale Many Predictions Fail--but Some Don't online

The Signal and online the Noise: Why So outlet online sale Many Predictions Fail--but Some Don't online
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The Signal and online the Noise: Why So outlet online sale Many Predictions Fail--but Some Don't online_top

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UPDATED FOR 2020 WITH A NEW PREFACE BY NATE SILVER

"One of the more momentous books of the decade." —The New York Times Book Review


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. He solidified his standing as the nation''s foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. 
 
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 to global pandemics, 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.

Review

“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 . . . could turn out to be  one of the more momentous books of the decade.”  New York Times Book Review

“Mr. Silver, just 34, is an expert at finding signal in noise . . . Lively prose—from energetic to outraged . . . illustrates his dos and don’ts through a series of interesting essays that examine how predictions are made in fields including chess, baseball, weather forecasting, earthquake analysis and politics… [the] chapter on global warming is one of the most objective and honest analyses I’ve seen . . . even the noise makes for a good read.” New York Times

"A serious treatise about the craft of prediction—without academic mathematics—cheerily aimed at lay readers. Silver''s coverage is polymathic, ranging from poker and earthquakes to climate change and terrorism."  —New York Review of Books

"Mr. Silver''s breezy style makes even the most difficult statistical material accessible. What is more, his arguments and examples are painstakingly researched . . ." Wall Street Journal

"Nate Silver is the Kurt Cobain of statistics . . . His ambitious new book,  The Signal and the Noise, is a practical handbook and a philosophical manifesto in one, following the theme of prediction through a series of case studies ranging from hurricane tracking to professional poker to counterterrorism. It will be a supremely valuable resource for anyone who wants to make good guesses about the future, or who wants to assess the guesses made by others. In other words, everyone." The Boston Globe

"Silver delivers an improbably breezy read on what is essentially a primer on making predictions." Washington Post
 
The Signal and the Noise is many things—an introduction to the Bayesian theory of probability, a meditation on luck and character, a commentary on poker''s insights into life—but it''s most important function is its most basic and absolutely necessary one right now: a guide to detecting and avoiding bullshit dressed up as data . . . What is most refreshing . . . is its humility. Sometimes we have to deal with not knowing, and we need somebody to tell us that.” Esquire

“[An] entertaining popularization of a subject that scares many people off . . .Silver’s journey from consulting to baseball analytics to professional poker to political prognosticating is very much that of a restless and curious mind. And this, more than number-crunching, is where real forecasting prowess comes from.” Slate

“Nate Silver serves as a sort of Zen master to American election-watchers . . . In the spirit of Nassim Nicholas Taleb’s widely read The Black Swan, Mr. Silver asserts that humans are overconfident in their predictive abilities, that they struggle to think in probabilistic terms and build models that do not allow for uncertainty.” The Economist

"Silver explores our attempts at forecasting stocks, storms, sports, and anything else not set in stone." Wired

"The Signal and the Noise is essential reading in the era of Big Data that touches every business, every sports event, and every policymaker."  —Forbes.com

“Laser sharp. Surprisingly, statistics in Silver’s hands is not without some fun.” Smithsonian Magazine

“A substantial, wide-ranging, and potentially important gauntlet of probabilistic thinking based on actual data thrown at the feet of a culture determined to sweep away silly liberal notions like ‘facts.’” The Village Voice

“Silver shines a light on 600 years of human intelligence-gathering—from the advent of the printing press all the way through the Industrial Revolution and up to the current day—and he finds that it''s been an inspiring climb. We''ve learned so much, and we still have so much left to learn.” —MLB.com

“Nate Silver’s  The Signal and the Noise is The Soul of a New Machine for the 21st century (a century we thought we’d be a lot better at predicting than we actually are). Our political discourse is already better informed and more data-driven because of Nate’s influence. But here he shows us what he has always been able to see in the numbers—the heart and the ethical imperative of getting the quantitative questions right. A wonderful read—totally engrossing." —Rachel Maddow, author of Drift
 
“Yogi Berra was right: ‘forecasting is hard, especially about the future.’ In this important book, Nate Silver explains why the performance of experts varies from prescient to useless and why we must plan for the unexpected. Must reading for anyone who cares about what might happen next.”  —Richard Thaler, co-author of Nudge

About the Author

Nate Silver is the founder and editor in chief of FiveThirtyEight.com.

Excerpt. © Reprinted by permission. All rights reserved.

At about the time The Signal and the Noise was first published in September 2012, “Big Data” was on its way becoming a Big Idea. Google searches for the term doubled over the course of a year,1 as did mentions of it in the news media.2 Hundreds of books were published on the subject. If you picked up any business periodical in 2013, advertisements for Big Data were as ubiquitous as cigarettes in an episode of Mad Men.
 
But by late 2014, there was evidence that trend had reached its apex. The frequency with which Big Data was mentioned in corporate press releases had slowed down and possibly begun to decline.3 The technology research firm Gartner even declared that Big Data had passed the peak of its “hype cycle.”4
 
I hope that Gartner is right. Coming to a better understanding of data and statistics is essential to help us navigate our lives. But as with most emerging technologies, the widespread benefits to science, industry, and human welfare will come only after the hype has died down.
 
FIGURE P-1: BIG DATA MENTIONS IN CORPORATE PRESS RELEASES
 
I worry that certain events in my life have contributed to the hype cycle. On November 6, 2012, the statistical model at my Web site FiveThirtyEight “called” the winner of the American presidential election correctly in all fifty states. I received a congratulatory phone call from the White House. I was hailed as “lord and god of the algorithm” by The Daily Show’s Jon Stewart. My name briefly received more Google search traffic than the vice president of the United States.
 
I enjoyed some of the attention, but I felt like an outlier—even a fluke. Mostly I was getting credit for having pointed out the obvious—and most of the rest was luck.*
 
To be sure, it was reasonably clear by Election Day that President Obama was poised to win reelection. When voters went to the polls on election morning, FiveThirtyEight’s statistical model put his chances of winning the Electoral College at about 90 percent.* A 90 percent chance is not quite a sure thing: Would you board a plane if the pilot told you it had a 90 percent chance of landing successfully? But when there’s only reputation rather than life or limb on the line, it’s a good bet. Obama needed to win only a handful of the swing states where he was tied or ahead in the polls; Mitt Romney would have had to win almost all of them.
 
But getting every state right was a stroke of luck. In our Election Day forecast, Obama’s chance of winning Florida was just 50.3 percent—the outcome was as random as a coin flip. Considering other states like Virginia, Ohio, Colorado, and North Carolina, our chances of going fifty-for-fifty were only about 20 percent.5 FiveThirtyEight’s “perfect” forecast was fortuitous but contributed to the perception that statisticians are soothsayers—only using computers rather than crystal balls.
 
This is a wrongheaded and rather dangerous idea. American presidential elections are the exception to the rule—one of the few examples of a complex system in which outcomes are usually more certain than the conventional wisdom implies. (There are a number of reasons for this, not least that the conventional wisdom is often not very wise when it comes to politics.) Far more often, as this book will explain, we overrate our ability to predict the world around us. With some regularity, events that are said to be certain fail to come to fruition—or those that are deemed impossible turn out to occur.
 
If all of this is so simple, why did so many pundits get the 2012 election wrong? It wasn’t just on the fringe of the blogosphere that conservatives insisted that the polls were “skewed” toward President Obama. Thoughtful conservatives like George F. Will6 and Michael Barone7 also predicted a Romney win, sometimes by near-landslide proportions.
 
One part of the answer is obvious: the pundits didn’t have much incentive to make the right call. You can get invited back on television with a far worse track record than Barone’s or Will’s—provided you speak with some conviction and have a viewpoint that matches the producer’s goals.
 
An alternative interpretation is slightly less cynical but potentially harder to swallow: human judgment is intrinsically fallible. It’s hard for any of us (myself included) to recognize how much our relatively narrow range of experience can color our interpretation of the evidence. There’s so much information out there today that none of us can plausibly consume all of it. We’re constantly making decisions about what Web site to read, which television channel to watch, and where to focus our attention.
 
Having a better understanding of statistics almost certainly helps. Over the past decade, the number of people employed as statisticians in the United States has increased by 35 percent8 even as the overall job market has stagnated. But it’s a necessary rather than sufficient part of the solution. Some of the examples of failed predictions in this book concern people with exceptional intelligence and exemplary statistical training—but whose biases still got in the way.
 
These problems are not so simple and so this book does not promote simple answers to them. It makes some recommendations but they are philosophical as much as technical. Once we’re getting the big stuff right—coming to a better understanding of probably and uncertainty; learning to recognize our biases; appreciating the value of diversity, incentives, and experimentation—we’ll have the luxury of worrying about the finer points of technique.
 
Gartner’s hype cycle ultimately has a happy ending. After the peak of inflated expectations there’s a “trough of disillusionment”—what happens when people come to recognize that the new technology will still require a lot of hard work.
 
FIGURE P-2: GARTNER’S HYPE CYCLE
 
But right when views of the new technology have begun to lapse from healthy skepticism into overt cynicism, that technology can begin to pay some dividends. (We’ve been through this before: after the computer boom in the 1970s and the Internet commerce boom of the late 1990s, among other examples.) Eventually it matures to the point when there are fewer glossy advertisements but more gains in productivity—it may even have become so commonplace that we take it for granted. I hope this book can accelerate the process, however slightly.
 
This is a book about information, technology, and scientific progress. This is a book about competition, free markets, and the evolution of ideas. This is a book about the things that make us smarter than any computer, and a book about human error. This is a book about how we learn, one step at a time, to come to knowledge of the objective world, and why we sometimes take a step back.
 
This is a book about prediction, which sits at the intersection of all these things. It is a study of why some predictions succeed and why some fail. My hope is that we might gain a little more insight into planning our futures and become a little less likely to repeat our mistakes.
 
More Information, More Problems
 
The original revolution in information technology came not with the microchip, but with the printing press. Johannes Gutenberg’s invention in 1440 made information available to the masses, and the explosion of ideas it produced had unintended consequences and unpredictable effects. It was a spark for the Industrial Revolution in 1775,1 a tipping point in which civilization suddenly went from having made almost no scientific or economic progress for most of its existence to the exponential rates of growth and change that are familiar to us today. It set in motion the events that would produce the European Enlightenment and the founding of the American Republic.
 
But the printing press would first produce something else: hundreds of years of holy war. As mankind came to believe it could predict its fate and choose its destiny, the bloodiest epoch in human history followed.2
 
Books had existed prior to Gutenberg, but they were not widely written and they were not widely read. Instead, they were luxury items for the nobility, produced one copy at a time by scribes.3 The going rate for reproducing a single manuscript was about one florin (a gold coin worth about $200 in today’s dollars) per five pages,4 so a book like the one you’re reading now would cost around $20,000. It would probably also come with a litany of transcription errors, since it would be a copy of a copy of a copy, the mistakes having multiplied and mutated through each generation.
 
This made the accumulation of knowledge extremely difficult. It required heroic effort to prevent the volume of recorded knowledge from actually decreasing, since the books might decay faster than they could be reproduced. Various editions of the Bible survived, along with a small number of canonical texts, like from Plato and Aristotle. But an untold amount of wisdom was lost to the ages,5 and there was little incentive to record more of it to the page.
 
The pursuit of knowledge seemed inherently futile, if not altogether vain. If today we feel a sense of impermanence because things are changing so rapidly, impermanence was a far more literal concern for the generations before us. There was “nothing new under the sun,” as the beautiful Bible verses in Ecclesiastes put it—not so much because everything had been discovered but because everything would be forgotten.6
 
The printing press changed that, and did so permanently and profoundly. Almost overnight, the cost of producing a book decreased by about three hundred times,7 so a book that might have cost $20,000 in today’s dollars instead cost $70. Printing presses spread very rapidly throughout Europe; from Gutenberg’s Germany to Rome, Seville, Paris, and Basel by 1470, and then to almost all other major European cities within another ten years.8 The number of books being produced grew exponentially, increasing by about thirty times in the first century after the printing press was invented.9 The store of human knowledge had begun to accumulate, and rapidly.
 
FIGURE I-1: EUROPEAN BOOK PRODUCTION
 
As was the case during the early days of the World Wide Web, however, the quality of the information was highly varied. While the printing press paid almost immediate dividends in the production of higher quality maps,10 the bestseller list soon came to be dominated by heretical religious texts and pseudoscientific ones.11 Errors could now be mass-produced, like in the so-called Wicked Bible, which committed the most unfortunate typo in history to the page: thou shalt commit adultery.12 Meanwhile, exposure to so many new ideas was producing mass confusion. The amount of information was increasing much more rapidly than our understanding of what to do with it, or our ability to differentiate the useful information from the mistruths.13 Paradoxically, the result of having so much more shared knowledge was increasing isolation along national and religious lines. The instinctual shortcut that we take when we have “too much information” is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest.
 
The most enthusiastic early customers of the printing press were those who used it to evangelize. Martin Luther’s Ninety-five Theses were not that radical; similar sentiments had been debated many times over. What was revolutionary, as Elizabeth Eisenstein writes, is that Luther’s theses “did not stay tacked to the church door.”14 Instead, they were reproduced at least three hundred thousand times by Gutenberg’s printing press15—a runaway hit even by modern standards.
 
The schism that Luther’s Protestant Reformation produced soon plunged Europe into war. From 1524 to 1648, there was the German Peasants’ War, the Schmalkaldic War, the Eighty Years’ War, the Thirty Years’ War, the French Wars of Religion, the Irish Confederate Wars, the Scottish Civil War, and the English Civil War—many of them raging simultaneously. This is not to neglect the Spanish Inquisition, which began in 1480, or the War of the Holy League from 1508 to 1516, although those had less to do with the spread of Protestantism. The Thirty Years’ War alone killed one-third of Germany’s population,16 and the seventeenth century was possibly the bloodiest ever, with the early twentieth staking the main rival claim.17
 
But somehow in the midst of this, the printing press was starting to produce scientific and literary progress. Galileo was sharing his (censored) ideas, and Shakespeare was producing his plays.
 
Shakespeare’s plays often turn on the idea of fate, as much drama does. What makes them so tragic is the gap between what his characters might like to accomplish and what fate provides to them. The idea of controlling one’s fate seemed to have become part of the human consciousness by Shakespeare’s time—but not yet the competencies to achieve that end. Instead, those who tested fate usually wound up dead.18
 
These themes are explored most vividly in The Tragedy of Julius Caesar. Throughout the first half of the play Caesar receives all sorts of apparent warning signs—what he calls predictions19 (“beware the ides of March”)—that his coronation could turn into a slaughter. Caesar of course ignores these signs, quite proudly insisting that they point to someone else’s death—or otherwise reading the evidence selectively. Then Caesar is assassinated.
 
“[But] men may construe things after their fashion / Clean from the purpose of the things themselves,” Shakespeare warns us through the voice of Cicero—good advice for anyone seeking to pluck through their newfound wealth of information. It was hard to tell the signal from the noise. The story the data tells us is often the one we’d like to hear, and we usually make sure that it has a happy ending.
 
And yet if The Tragedy of Julius Caesar turned on an ancient idea of prediction—associating it with fatalism, fortune-telling, and superstition—it also introduced a more modern and altogether more radical idea: that we might interpret these signs so as to gain an advantage from them. “Men at some time are masters of their fates,” says Cassius, hoping to persuade Brutus to partake in the conspiracy against Caesar.
 
The idea of man as master of his fate was gaining currency. The words predict and forecast are largely used interchangeably today, but in Shakespeare’s time, they meant different things. A prediction was what the soothsayer told you; a forecast was something more like Cassius’s idea.
 
The term forecast came from English’s Germanic roots,20 unlike predict, which is from Latin.21 Forecasting reflected the new Protestant worldliness rather than the otherworldliness of the Holy Roman Empire. Making a forecast typically implied planning under conditions of uncertaintyIt suggested having prudence, wisdom, and industriousness, more like the way we now use the word foresight. 22
 
The theological implications of this idea are complicated.23 But they were less so for those hoping to make a gainful existence in the terrestrial world. These qualities were strongly associated with the Protestant work ethic, which Max Weber saw as bringing about capitalism and the Industrial Revolution.24 This notion of forecasting was very much tied in to the notion of progress. All that information in all those books ought to have helped us to plan our lives and profitably predict the world’s course.
 
•   •   •
 
The Protestants who ushered in centuries of holy war were learning how to use their accumulated knowledge to change society. The Industrial Revolution largely began in Protestant countries and largely in those with a free press, where both religious and scientific ideas could flow without fear of censorship.25
 
The importance of the Industrial Revolution is hard to overstate. Throughout essentially all of human history, economic growth had proceeded at a rate of perhaps 0.1 percent per year, enough to allow for a very gradual increase in population, but not any growth in per capita living standards.26 And then, suddenly, there was progress when there had been none. Economic growth began to zoom upward much faster than the growth rate of the population, as it has continued to do through to the present day, the occasional global financial meltdown notwithstanding.27
 

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4.4 out of 54.4 out of 5
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Robert Morris
5.0 out of 5 starsVerified Purchase
How and why, more often than not, "human judgment is intrinsically fallible"
Reviewed in the United States on January 16, 2018
This book was first published in 2012, at a time when Big Data (or if you prefer, big data) was only beginning to receive the attention it deserves as a better way to use analytics within and beyond the business world. One key point is that big data should also be right... See more
This book was first published in 2012, at a time when Big Data (or if you prefer, big data) was only beginning to receive the attention it deserves as a better way to use analytics within and beyond the business world. One key point is that big data should also be right data and in sufficient quantity. I recently re-read the book, in its paperbound edition. Thde quality and value of its insights have held up remarkably well.

In the years that followed publication of the first edition, as Nate Silver notes in the new Preface, the perception that statisticians are soothsayers was proven to be an exaggeration, at best, and a dangerous assumption, at worst. This new edition "makes some recommendations but they are philosophical as much as technical. Once we''re getting the big stuff right -- coming to a better [i.e. more accurate and more reliable] understanding of probability and uncertainty; learning to recognize our biases; appreciating the value of diversity, incentives, and experimentation -- we''ll have the luxury of worrying about the finer points of technique."

In the Introduction to the First Edition, Silver observes, "If there is one thing that defines Americans -- one thing that makes us exceptional -- it is our belief in Cassius'' idea that we are in control of our own fates." In t his instance, Silver refers to a passage in Shakespeare''s play, Julius Caesar, when Cassius observes:

"Men at some time are masters of their fates.
The fault, dear Brutus, is not in our stars,
But in ourselves, that we are underlings."
(Act 1, Scene 2, Lines 146-148)

Cassius'' assertion has serious implications and significant consequences. It is directly relevant to a theory named after Reverend Thomas Bayes (1701–1761), who first provided an equation that allows new evidence to update beliefs in his An Essay towards solving a Problem in the Doctrine of Chances (1763). Silver: "Bayes''s theorem is nominally a mathematical formula. But it is really much more than that. It implies that we must think differently about our ideas [predictions, for example] -- and how to test them. We must become more comfortable with probability and uncertainty. We must think more carefully about the assumptions and beliefs that we bring to a problem."

Silver cites another passage in Julius Caesar when Cicero warns Caesar: "Men may construe things, after their fashion / Clean from the purpose of things themselves." According to Silver, man perceives information selectively, subjectively, "and without much self-regard for the distortions this causes. We think we want information when we want knowledge." I take "want" to have a double meaning: lack and desire. Silver goes on to suggest, "the signal is the truth. The noise is what distracts us from the truth. This is a book about the signal and the noise...We may focus on those signals that advance our preferred theory about the world, or might imply a more optimistic outcome. Or we may simply focus on the ones that fit with bureaucratic protocol, like the doctrine that sabotage rather than an air attack was the more likely threat to Pearl Harbor."

In their review of the book for The New Yorker (January 25, 2013), Gary Marcus and Ernest Davis observe: "Switching to a Bayesian method of evaluating statistics will not fix the underlying problems; cleaning up science requires changes to the way in which scientific research is done and evaluated, not just a new formula." That is, we need to think about how we think so that we can make better decisions.

In Thinking, Fast and Slow, Daniel Kahneman explains how an easy question ("How coherent is the narrative of a given situation?") is often substituted for a more difficult one ("How probable is it?"). And this, according to Kahneman, is the source of many of the biases that infect our thinking. Kahneman and Tversky''s System 1 jumps to an intuitive conclusion based on a “heuristic” — an easy but imperfect way of answering hard questions — and System 2 lazily endorses this heuristic answer without bothering to scrutinize whether it is logical). And this, according to Kahneman, is the source of many of the biases that infect our thinking. System 1 jumps to an intuitive conclusion based on a “heuristic” — an easy but imperfect way of answering hard questions — and System 2 lazily endorses this heuristic answer without bothering to scrutinize whether it is logical.

When an unprecedented disaster occurs, some people may feel at least some doubt that they are in control of their fate. Nate Silver offers this reminder: "But our bias is to think we are better at prediction than we really are. The first twelve months of the new millennium have been rough, with one unpredicted disaster after another. May we arise from the ashes of these beaten but not bowed, a little more modest about our forecasting abilities, and a little less likely to repeat our mistakes."

A Jewish proverb suggests that man plans and then God laughs. The same could be said of man''s predictions.
40 people found this helpful
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Vagabond of Letters
3.0 out of 5 starsVerified Purchase
A good book from excellent author and uncannily-successful political predictor Nate ...
Reviewed in the United States on February 27, 2018
A good book from excellent author and uncannily-successful political predictor Nate Silver in great need of an editor. With a text 500pp long (without back matter), but still rather factual and straightforward (unlike the guilty pleasure purple prose of Taleb or Stephenson)... See more
A good book from excellent author and uncannily-successful political predictor Nate Silver in great need of an editor. With a text 500pp long (without back matter), but still rather factual and straightforward (unlike the guilty pleasure purple prose of Taleb or Stephenson) it''s very repetitive: entire sections repeat - with examples from other spheres of prediction, whether sports, stocks, or weather being the only fundamental differences - in which we learn that Silver made a living at online poker (and possibly online trading) before he made one as an online popular statistician - leading to a book 200pp or more over its Platonic length. No one will call Silver laconic, as the signal rather ironically gets lost in the noise.
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JD
4.0 out of 5 starsVerified Purchase
Statistics in Context + Important Key Takeaways
Reviewed in the United States on May 26, 2017
The author Nate Silver does a great job weaving more technical statistical concepts in context early in the book, so as not to lose readers early on. However I thought this would lead to more a detailed technical discussion later on, which the author said it would, but it... See more
The author Nate Silver does a great job weaving more technical statistical concepts in context early in the book, so as not to lose readers early on. However I thought this would lead to more a detailed technical discussion later on, which the author said it would, but it never really transpired. Instead he kept to analogies and keeping the science of prediction in context. Which there''s really nothing wrong with, if you''re someone looking for that ... just not exactly what I wanted or expected.

Nonetheless it''s a great book, and Silver bears the hallmark of someone who is intellectually curious and genuinely interested in making his analytical tool better, rather than attaching his ego to the outcome. As part of that, he''s refreshingly candid in his opinion of others. Well researched and covers a lot of areas including sports, weather, financial meltdowns, chess, and others. The best section imo was on chess, where he displayed both his story telling skills (retelling of chess master Kasparov''s loss to IBM was both compelling and insightful), and more in depth technical discussion which chess lends itself to. The book seemed to run out of steam toward the end, with some chapters going on longer than I thought necessary, particularly poker and efficient markets.

He shares some of my core beliefs that statistics/data is not enough, if you really want to understand something and make good forecasts you need to understand its underlying structure. And that the proper relationship between man and machine is symbiotic, rather than one taking over the other. Those, and the importance of thinking probabilistically, are the core takeaways.
17 people found this helpful
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Matthew RapaportTop Contributor: Philosophy
5.0 out of 5 starsVerified Purchase
Long but well written account of what it is difficult to make accurate predictions and what can be done about it
Reviewed in the United States on March 26, 2018
This is a book about forecasting; not a "how to" exactly, but a "how to make better". It is about why forecasts so often go wrong. They are hard to do right, and they are even harder to do exactly. Good forecasters know this and express results in terms of... See more
This is a book about forecasting; not a "how to" exactly, but a "how to make better". It is about why forecasts so often go wrong. They are hard to do right, and they are even harder to do exactly. Good forecasters know this and express results in terms of likelihoods, or margins of error. Bad forecasters often do not even care that their too-exact predictions are frequently, even almost always, wrong. Such people are "in the business" because the media attention such forecasts often receive has made them well off. Early in this very long book, Nate Silver gets into this. He calls such forecasters hedgehogs because they rely on a single strategy. By contrast, the foxes recognize that there is a lot to understand about the world, a lot that matters to what will happen in the future. This book is about the foxes, but even they are often wrong because what they are doing is hard, and this book is about why it is hard.

Silver rests his methodology on Thomas Bayes (and his subsequent champion Simon Laplace) and an approach to statistical reasoning called Bayesian Reasoning. Today this process is well known in the scientific and philosophical communities. Economists and sociologists are also fans, though its competitor, Frequentism, developed by Ronald Fisher some 190 years after Bayes, is even better known. Frequentism is what much of the "measures of significance" in widespread use today are about. It has certainly given us insight into the probabilistic nature of the world. But as Silver argues, Bayes does better when we must start from somewhere and project the future. Bayes gives us a way to refine projections as they evolve into the further future. Bayesianism is not only about the probabilistic nature of the world, but also about the incompleteness of our knowledge. Silver does not claim that Bayes is a magic bullet that will give you a correct forecast. Properly applied in areas where new data is accumulated, it will refine the next forecast, and more so those that follow. That is the point of it all. We can rarely hit the bulls eye, but we can approach it with each new try.

Silver walks us through various kinds of real-world examples where forecasting is important for one reason or another. Games like baseball, basketball, poker, and chess make up his first class of examples. Every one of these presents the keen observer with signals and noise of different kinds and the means by which we can separate these and properly understand where the signals point is crucial to improving our predictions about the future. From games he moves on to such things as weather, earthquakes, the economy, politics, military preparedness, and climate change. At the end he deals with the issue of terrorism.

His examples are chosen to illustrate how many different kinds of signals and noise there are. In some arenas there is so much signal, so many relations between factors have an influence on outcomes, that the signal itself becomes its own noise. For each arena explored he cites examples successful and unsuccessful forecasting and from a position of hindsight explains how it was that the forecasts came out as they did. What part of the signal was properly interpreted or missed altogether? What part of the noise was mistaken for signal? Which models were too simple, grasping signal but not enough of it, and which rested mostly on noise mistaken for signal. In each of his examples he returns to Bayes.

Silver never tells us how to get rid of the noise. He cannot. A great part of his point here is that we usually do not know, exactly, what is signal and what is noise in the data. When there is a clear cut causal connection, for example that increasing CO2 concentrations in the atmosphere must have a warming effect on the climate, we know we have some handle on real signal. But even a causal connection can be drowned out, at least in the short term, by other factors. He is careful to note again and again, that telling signal from noise can be very hard to do and often the best we can hope for is to understand that a wide latitude of likely possibility remains.

This is a long book. Its principles could be stated in a few pages, but its richness comes from Silver''s careful explication of signal and noise in each of the arenas he explores all of them very different. This explication requires a lot of pages, but that is the meat of the book. At the same time, Silver''s explanations are all plain, his writing about all of these subjects is easy to understand. Well done, and a book to which everyone with some forecasting to do should pay attention.
8 people found this helpful
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Awetabon
4.0 out of 5 starsVerified Purchase
A VERY hard book!
Reviewed in the United States on December 2, 2018
I liked the things I’ve learned about prediction. I disliked how hard it was! I feel like I just took a course in statistics or something... I had to reread some lines several times. I would recommend it to academic minded people who want to go really in depth into... See more
I liked the things I’ve learned about prediction. I disliked how hard it was! I feel like I just took a course in statistics or something... I had to reread some lines several times. I would recommend it to academic minded people who want to go really in depth into prediction, I would’ve been alright with just a cliff notes version. I chose a 4 star because the information was good, but not a 5 because it was wordy and hard to follow. But I understand also that he goes very in depth so for others who are extremely curious and bright this might’ve been a 5 for them.
Also I expected something like the usual pop psychology book that would go something like, “people will predict x but they don’t realize it’s actually y because their brains do z.” Instead it’s more like, “this is situation x, where you infer y, and receive a function of x and y, and also z, but don’t forget a, b and c, compared to g, and sometimes h, but that’s okay because i, do you see how z leads to w? Here is a graph of k.”
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J. Gordon
3.0 out of 5 starsVerified Purchase
Learned about statistics and economics but...
Reviewed in the United States on May 9, 2019
I loved the first few chapters but the sections on baseball and poker ground my enthusiasm down. The exposé / post mortem of the 2007 economic collapse alone makes the book worth buying. There’s a vibe of Jack of All Trades, Master of None, in the... See more
I loved the first few chapters but the sections on baseball and poker ground my enthusiasm down.

The exposé / post mortem of the 2007 economic collapse alone makes the book worth buying.

There’s a vibe of Jack of All Trades, Master of None, in the author’s sweeping examination of so many fields and subjects, essentially making a case that using statistics lets him dominate understanding of everything. Only when statistics as judge declares that something can’t be predicted or understood, is it so, keeping itself enthroned even in failure.

Summary
A Bayesian view is good to understand and practice. Random info almost always looks like non-random patterns and fools people. Behavioral economics shows people are biased. Lots of anecdotes.
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L. R.
5.0 out of 5 starsVerified Purchase
Well-researched and presented
Reviewed in the United States on April 16, 2018
This book was interesting, although I''m not sure I took away anything from it that I will use in my everyday life. Silver has a wide-ranging approach: the book is about forecasting in general, and so tackles a variety of areas where humans attempt to forecast from weather... See more
This book was interesting, although I''m not sure I took away anything from it that I will use in my everyday life. Silver has a wide-ranging approach: the book is about forecasting in general, and so tackles a variety of areas where humans attempt to forecast from weather (surprisingly good at this!) to earthquakes (lol terrible) to the stock market to baseball and on.

I have a friend who often talks about future events in his own life in probabilistic terms: a 90% chance he will go on this planned trip, or a 50% chance that he will retire this year, or whatever. Reading Silver''s book gave me a new appreciation for this approach, because Silver encourages the reader to think of forecasts in terms of probability and especially to think about uncertainty. Not only "what don''t you know?" but "what don''t you know that you don''t know?"

He skewers one particular target in the housing market crash: the rating agencies. The two major rating agencies emerged almost unscathed from the mortgage crisis, despite being in large part responsible for it. Yes, banks made sub-prime mortgages to people with terrible credit, and people with terrible credit dove into the market, and Fannie Mae and Freddie Mac underwrote those loans, and other banks came up with the bright idea of selling them by bundling them together and re-dividing them into tranches.. But it was the rating who''d given triple-A ratings to the "least risky" tranches of these high-risk mortgages. They''re the ones who said not "the housing market won''t crash" but "these investments are safe even if the housing market crashes.".

(Narrator: they were not safe.)

Anyway, this was a scholarly book (so many footnotes!) written with a solid, engaging style. Easy-to-follow and interesting. If you are interested in forecasting or probabilities as applied to real life, it''s an excellent read.
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Jeff and Tonya
5.0 out of 5 starsVerified Purchase
Solid Application of Statistics.
Reviewed in the United States on September 26, 2018
I''m a math geek who has casually followed Silver''s work since he came on the national radar after the 2008 Presidential election. In this book, he uses his own mathematical background and many interviews to show how probabilistic statistics (vs more deterministic... See more
I''m a math geek who has casually followed Silver''s work since he came on the national radar after the 2008 Presidential election. In this book, he uses his own mathematical background and many interviews to show how probabilistic statistics (vs more deterministic statistics) gives us great insight into a wide range of issues, from the mundane yet popular topics of poker and baseball - things he has personal experience with using statistics on - to the seemingly more substantial issues including weather forecasting, political polling, climate change and even terrorism. And overall, he is very careful to stick to his central point: follow the numbers, no matter where they lead - which he calls the "signal". Very highly recommended for anyone trying to have a genuine discussion on really almost any topic.
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Francisco Inacio Bastos
5.0 out of 5 starsVerified Purchase
Livro brilhante, bem escrito e divertido
Reviewed in Brazil on December 10, 2020
Por razões profissionais e também por gosto, leio com frequência livros de estatística, tanto técnicos como de divulgação. Raras vezes tive a oportunidade de ler algo tão claro, bem escrito e bem-humorado. Confesso não ter gostado muito do capítulo sobre a estatística do...See more
Por razões profissionais e também por gosto, leio com frequência livros de estatística, tanto técnicos como de divulgação. Raras vezes tive a oportunidade de ler algo tão claro, bem escrito e bem-humorado. Confesso não ter gostado muito do capítulo sobre a estatística do baseball, mas isso se deve tão-somente ao fato de achar o esporte incrivelmente monótono (obviamente, os fãs do esporte deverão adorar o referido capítulo). Feito esse reparo, absolutamente pessoal, os demais capítulos são ótimos, por vezes, brilhantes (não há outra definição), com uma exposição incrivelmente clara sobre estatística bayesiana e suas inúmeras aplicações práticas.
Por razões profissionais e também por gosto, leio com frequência livros de estatística, tanto técnicos como de divulgação. Raras vezes tive a oportunidade de ler algo tão claro, bem escrito e bem-humorado. Confesso não ter gostado muito do capítulo sobre a estatística do baseball, mas isso se deve tão-somente ao fato de achar o esporte incrivelmente monótono (obviamente, os fãs do esporte deverão adorar o referido capítulo). Feito esse reparo, absolutamente pessoal, os demais capítulos são ótimos, por vezes, brilhantes (não há outra definição), com uma exposição incrivelmente clara sobre estatística bayesiana e suas inúmeras aplicações práticas.
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Tiago Irineu
5.0 out of 5 starsVerified Purchase
Many examples about probabilistic thinking, with an underlying defense of Bayesian statistics
Reviewed in Brazil on August 30, 2021
The book could a bit more of theoretical discussion, but it gives a reasonable introduction to probabilistic thinking and how it is used or mis(used) in daily life, with examples ranging from sports to financial markets. Given Nate''s background it is not surprising that he...See more
The book could a bit more of theoretical discussion, but it gives a reasonable introduction to probabilistic thinking and how it is used or mis(used) in daily life, with examples ranging from sports to financial markets. Given Nate''s background it is not surprising that he focuses on forecasting, and how to develop a better framework for becoming a better forecaster, and also how a lack of probabilistic education and communication lead people astray, even leading to mistake that cost lives of thousand of people. My ultimate take of this book would be that we should consider more deeply the possible impacts of probability in our lives, and also that data does not speak for itself. Data need context and for gaining real insights it''s necessary to apply critical thinking to it.
The book could a bit more of theoretical discussion, but it gives a reasonable introduction to probabilistic thinking and how it is used or mis(used) in daily life, with examples ranging from sports to financial markets.

Given Nate''s background it is not surprising that he focuses on forecasting, and how to develop a better framework for becoming a better forecaster, and also how a lack of probabilistic education and communication lead people astray, even leading to mistake that cost lives of thousand of people.

My ultimate take of this book would be that we should consider more deeply the possible impacts of probability in our lives, and also that data does not speak for itself. Data need context and for gaining real insights it''s necessary to apply critical thinking to it.
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Alessandro
3.0 out of 5 starsVerified Purchase
Buon titolo
Reviewed in Italy on October 7, 2013
Fare previsioni non è affatto semplice. Anche se la mole di informazioni disponibili aumenta a ritmo vertiginoso, la quantità di verità e segnali utili alla nostra conoscenza del mondo non tiene lo stesso passo. La maggior parte è solo interferenza e il rumore sta crescendo...See more
Fare previsioni non è affatto semplice. Anche se la mole di informazioni disponibili aumenta a ritmo vertiginoso, la quantità di verità e segnali utili alla nostra conoscenza del mondo non tiene lo stesso passo. La maggior parte è solo interferenza e il rumore sta crescendo molto più che il segnale. Nate Silver, statistico, uno dei pensatori più originali dell''ultima generazione, affronta un tema centrale nella vita di tutti poter basare le proprie scelte su previsioni che si riveleranno affidabili nel tempo con uno stile brillante, trasformando anche le questioni più teoriche in qualcosa di divertente, interessante e necessario. Dalla politica all''economia, passando per i tanti campi di applicazione della statistica nella vita quotidiana, dal poker alla meteorologia, dai terremoti al gioco degli scacchi, fino alla possibilità di scoprire il tradimento di un partner ritrovando un indumento sospetto in giro per casa. Dopo aver letto "Il Segnale e il Rumore" imparerete a prestare attenzione alle previsioni del meteo per i giorni seguenti ma a diffidare da quelle che vanno oltre la settimana. Darete un peso diverso ai sondaggi politici e a come investite il denaro in borsa. Capirete che non è possibile prevedere quando ci sarà il prossimo devastante terremoto ma che potreste mettervi in salvo in caso di uragano.
Fare previsioni non è affatto semplice. Anche se la mole di informazioni disponibili aumenta a ritmo vertiginoso, la quantità di verità e segnali utili alla nostra conoscenza del mondo non tiene lo stesso passo. La maggior parte è solo interferenza e il rumore sta crescendo molto più che il segnale. Nate Silver, statistico, uno dei pensatori più originali dell''ultima generazione, affronta un tema centrale nella vita di tutti poter basare le proprie scelte su previsioni che si riveleranno affidabili nel tempo con uno stile brillante, trasformando anche le questioni più teoriche in qualcosa di divertente, interessante e necessario. Dalla politica all''economia, passando per i tanti campi di applicazione della statistica nella vita quotidiana, dal poker alla meteorologia, dai terremoti al gioco degli scacchi, fino alla possibilità di scoprire il tradimento di un partner ritrovando un indumento sospetto in giro per casa. Dopo aver letto "Il Segnale e il Rumore" imparerete a prestare attenzione alle previsioni del meteo per i giorni seguenti ma a diffidare da quelle che vanno oltre la settimana. Darete un peso diverso ai sondaggi politici e a come investite il denaro in borsa. Capirete che non è possibile prevedere quando ci sarà il prossimo devastante terremoto ma che potreste mettervi in salvo in caso di uragano.
One person found this helpful
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KIRAN
4.0 out of 5 starsVerified Purchase
Excellent
Reviewed in India on November 24, 2018
Good book to read. Though it is not justifying on how he predicted US elections. Book is mostly on PROBABILITY equations. Yes for sure probability may not halp in predicting all future events but it is surely help. Good book to read but i want more on step by step guide on...See more
Good book to read. Though it is not justifying on how he predicted US elections. Book is mostly on PROBABILITY equations. Yes for sure probability may not halp in predicting all future events but it is surely help. Good book to read but i want more on step by step guide on how has he achieved sone of good predictions.
Good book to read. Though it is not justifying on how he predicted US elections. Book is mostly on PROBABILITY equations. Yes for sure probability may not halp in predicting all future events but it is surely help. Good book to read but i want more on step by step guide on how has he achieved sone of good predictions.
One person found this helpful
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Dr. Carlo N. Colacino
2.0 out of 5 starsVerified Purchase
20-80
Reviewed in Italy on September 23, 2014
This book is the perfect exemplification of the Pareto principle: 20% of the book is really good, full of useful information, 80% is useless - see the chapter about the sport bettor and the last one, about terrorism- and poorly written. I expected much more from it. I am...See more
This book is the perfect exemplification of the Pareto principle: 20% of the book is really good, full of useful information, 80% is useless - see the chapter about the sport bettor and the last one, about terrorism- and poorly written. I expected much more from it. I am disappointed.
This book is the perfect exemplification of the Pareto principle: 20% of the book is really good, full of useful information, 80% is useless - see the chapter about the sport bettor and the last one, about terrorism- and poorly written. I expected much more from it. I am disappointed.
One person found this helpful
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