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title: "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

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NEW YORK TIMES BESTSELLER • The groundbreaking exploration of probability and uncertainty that explains how to make better predictions in a world drowning in data, from the nation’s foremost political forecaster—updated with insights into the pandemic, journalism today, and polling One of The Wall Street Journal ’s Ten Best Works of Nonfiction of the Year “Could turn out to be one of the more momentous books of the decade.”— The New York Times Book Review Most predictions fail, often at great cost to society, because 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. Drawing on his own groundbreaking work in sports and politics, Nate Silver examines the world of prediction, investigating how to seek truth from data. In The Signal and the Noise , Silver visits innovative 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 discovers that what the most accurate ones have in common is a superior command of probability—as well as a healthy dose of humility. With everything from the global economy to the fight against disease hanging on the quality of our predictions, Nate Silver’s insights are an essential read.

Review: Much-needed insight to understand and improve predictive science - This is the best general-readership book on applied statistics that I've read. Short review: if you're interested in science, economics, or prediction: read it. It's full of interesting cases, builds intuition, and is a readable example of Bayesian thinking. Longer review: I'm an applied business researcher and that means my job is to deliver quality forecasts: to make them, persuade people of them, and live by the results they bring. Silver's new book offers a wealth of insight for many different audiences. It will help you to develop intuition for the kinds of predictions that are possible, that are not so possible, where they may go wrong, and how to avoid some common pitfalls. The core concept is this: prediction is a vital part of science, of business, of politics, of pretty much everything we do. But we're not very good at it, and fall prey to cognitive biases and other systemic problems such as information overload that make things worse. However, we are simultaneously learning more about how such things occur and that knowledge can be used to make predictions better -- and to improve our models in science, politics, business, medicine, and so many other areas. The book presents real-world experience and critical reflection on what happens to research in social contexts. Data-driven models with inadequate theory can lead to terrible inferences. For example, on p. 162: "What happens in systems with noisy data and underdeveloped theory - like earthquake prediction and parts of economic and political science - is a two-step process. First, people start to mistake the noise for a signal. Second, this noise pollutes journals, blogs, and news accounts with false alarms, undermining good science and setting back our ability to understand how the system really works." This is the kind of insight that every good practitioner acquires through hard-won battles, and continues to wrestle every day both in doing work and in communicating it to others. It is both readable and technically accurate: it presents just enough model details yet avoids being formula-heavy. Statisticians will be able to reproduce models similar to the ones he discusses, but general readers will not be left out: the material is clear and applicable. Scholars of all stripes will appreciate the copious notes and citations, 56 pages of notes and another 20 pages of index, which detail the many sources. It is also important to note that this is perhaps the best general readership book from a Bayesian perspective -- a viewpoint that is overdue for readable exposition. The models cover a diversity of areas from baseball to politics, from earthquakes to finance, from climate science to chess. Of course this makes the book fascinating to generalists, geeks, and breadth thinkers, but perhaps more importantly, I think it serves well to develop reusable intuition across domains. And, for those of us who practice such things professionally, to bring stories and examples that we can tell and use to illustrate concepts with the people we inform. There are three audiences who might not appreciate the book as much. First are students looking for a how-to book. Silver provides a lot of pointers and examples, but does not get into nuts and bolts details or supply foundational technical instruction. That requires coursework in research methods and and statistics. Second, his approach to doing multiple models and interpreting them humbly will not satisfy those who promote a naive, gee-whiz, "look how great these new methods are" approach to research. But then, that's not a problem; it's a good thing. The third non-fitting audience will be experts who desire depth in one of the book's many topic areas; it's not a technical treatise for them and I can confidently predict grumbling in some quarters. Overall, those three audiences are small, which happily leaves the rest of us to enjoy the book. What would make it better? As a pro, I'd like a little more depth (of course). It emphasizes games a little too much for my taste. And a clearer prescriptive framework could be nice (but also could be a problem for reasons he illustrates). But those are minor points; it hits its target better than any other such book I know. Conclusion: if you're interested in scientific or statistical forecasting, either as a professional or layperson, or if you simply enjoy general science books, get it. Cheers!
Review: Mostly good stuff, but a little disappointing - I had high hopes for this book. A book on forecasting by someone who has actually been successful at it-what could be better than that. ===The Good Stuff=== * Silver is a fairly honest writer. He is able to describe his previous forecasts, the methodology he used, and is frank with himself and the reader about what failed and what worked. * Unlike some other books on this subject, Silver comes across as actually understanding the theory behind probability, and knows how to apply it to everyday problems. * The material is chosen from a variety of topics, with different constraints and limitations. Chess, which is relatively deterministic but has a large number of permutations is very different that weather, which follows a few simple laws but requires intensive calculation to forecast. Silver explains how both of these create different challenges, and techniques for overcoming those problems. * He avoids the usual "bag of red and black ping-pong ball" nonsense that usually clutters up books on this subject, and his discussion of Bayesian probability analysis is first-rate. ===The Not-So-Good Stuff=== * I found the book a little tough to read. Silver writes well enough, but the material is presented in a much drier and more formal manner than other books such as Freakonomics. This is my major issue with the book. It is not a "mass market, fun to read" book, but neither is it a rigorous treatment of a mathematical subject. Rather it sort of languishes in a no-mans-land between the two. And for those of us who at least think we understand statistics, some of the passages in the book are frustrating as you try to decode what Silver is really talking about. * Silver has a bad habit of interjecting his own opinions and thoughts into the discussion. For example, his discussion of economic forecasting gets mired down in his own opinions on government spending levels and priorities. It takes away from the objectivity, and I know Silver knows better. ===Conclusion=== Even though it took me over a week to read this book, (a very long time for me), I enjoyed most of it. I did learn a few things about how forecasts work, and how to spot the difficulties in forecasting any given event. However it is more of a high-level look, and you will not learn much about how to actually forecast anything from this book. I would have preferred the book to either be more entertaining (Moneyball), or more "textbooky" rather than trying to be a little of both. In some ways, I think I would have enjoyed it more if I knew less about the math of statistics. Still, if you are at all interested in how complicated processes like weather, chess and national economies are forecast, it is well worth a read.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #43,658 in Books ( See Top 100 in Books ) #4 in Business Planning & Forecasting (Books) #8 in Elections #19 in Probability & Statistics (Books) |
| Customer Reviews | 4.4 out of 5 stars 3,905 Reviews |

## Images

![The Signal and the Noise: Why So Many Predictions Fail--but Some Don't - Image 1](https://m.media-amazon.com/images/I/81oepVWeLiL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Much-needed insight to understand and improve predictive science
*by S***R on September 27, 2012*

This is the best general-readership book on applied statistics that I've read. Short review: if you're interested in science, economics, or prediction: read it. It's full of interesting cases, builds intuition, and is a readable example of Bayesian thinking. Longer review: I'm an applied business researcher and that means my job is to deliver quality forecasts: to make them, persuade people of them, and live by the results they bring. Silver's new book offers a wealth of insight for many different audiences. It will help you to develop intuition for the kinds of predictions that are possible, that are not so possible, where they may go wrong, and how to avoid some common pitfalls. The core concept is this: prediction is a vital part of science, of business, of politics, of pretty much everything we do. But we're not very good at it, and fall prey to cognitive biases and other systemic problems such as information overload that make things worse. However, we are simultaneously learning more about how such things occur and that knowledge can be used to make predictions better -- and to improve our models in science, politics, business, medicine, and so many other areas. The book presents real-world experience and critical reflection on what happens to research in social contexts. Data-driven models with inadequate theory can lead to terrible inferences. For example, on p. 162: "What happens in systems with noisy data and underdeveloped theory - like earthquake prediction and parts of economic and political science - is a two-step process. First, people start to mistake the noise for a signal. Second, this noise pollutes journals, blogs, and news accounts with false alarms, undermining good science and setting back our ability to understand how the system really works." This is the kind of insight that every good practitioner acquires through hard-won battles, and continues to wrestle every day both in doing work and in communicating it to others. It is both readable and technically accurate: it presents just enough model details yet avoids being formula-heavy. Statisticians will be able to reproduce models similar to the ones he discusses, but general readers will not be left out: the material is clear and applicable. Scholars of all stripes will appreciate the copious notes and citations, 56 pages of notes and another 20 pages of index, which detail the many sources. It is also important to note that this is perhaps the best general readership book from a Bayesian perspective -- a viewpoint that is overdue for readable exposition. The models cover a diversity of areas from baseball to politics, from earthquakes to finance, from climate science to chess. Of course this makes the book fascinating to generalists, geeks, and breadth thinkers, but perhaps more importantly, I think it serves well to develop reusable intuition across domains. And, for those of us who practice such things professionally, to bring stories and examples that we can tell and use to illustrate concepts with the people we inform. There are three audiences who might not appreciate the book as much. First are students looking for a how-to book. Silver provides a lot of pointers and examples, but does not get into nuts and bolts details or supply foundational technical instruction. That requires coursework in research methods and and statistics. Second, his approach to doing multiple models and interpreting them humbly will not satisfy those who promote a naive, gee-whiz, "look how great these new methods are" approach to research. But then, that's not a problem; it's a good thing. The third non-fitting audience will be experts who desire depth in one of the book's many topic areas; it's not a technical treatise for them and I can confidently predict grumbling in some quarters. Overall, those three audiences are small, which happily leaves the rest of us to enjoy the book. What would make it better? As a pro, I'd like a little more depth (of course). It emphasizes games a little too much for my taste. And a clearer prescriptive framework could be nice (but also could be a problem for reasons he illustrates). But those are minor points; it hits its target better than any other such book I know. Conclusion: if you're interested in scientific or statistical forecasting, either as a professional or layperson, or if you simply enjoy general science books, get it. Cheers!

### ⭐⭐⭐⭐ Mostly good stuff, but a little disappointing
*by A***N on November 8, 2012*

I had high hopes for this book. A book on forecasting by someone who has actually been successful at it-what could be better than that. ===The Good Stuff=== * Silver is a fairly honest writer. He is able to describe his previous forecasts, the methodology he used, and is frank with himself and the reader about what failed and what worked. * Unlike some other books on this subject, Silver comes across as actually understanding the theory behind probability, and knows how to apply it to everyday problems. * The material is chosen from a variety of topics, with different constraints and limitations. Chess, which is relatively deterministic but has a large number of permutations is very different that weather, which follows a few simple laws but requires intensive calculation to forecast. Silver explains how both of these create different challenges, and techniques for overcoming those problems. * He avoids the usual "bag of red and black ping-pong ball" nonsense that usually clutters up books on this subject, and his discussion of Bayesian probability analysis is first-rate. ===The Not-So-Good Stuff=== * I found the book a little tough to read. Silver writes well enough, but the material is presented in a much drier and more formal manner than other books such as Freakonomics. This is my major issue with the book. It is not a "mass market, fun to read" book, but neither is it a rigorous treatment of a mathematical subject. Rather it sort of languishes in a no-mans-land between the two. And for those of us who at least think we understand statistics, some of the passages in the book are frustrating as you try to decode what Silver is really talking about. * Silver has a bad habit of interjecting his own opinions and thoughts into the discussion. For example, his discussion of economic forecasting gets mired down in his own opinions on government spending levels and priorities. It takes away from the objectivity, and I know Silver knows better. ===Conclusion=== Even though it took me over a week to read this book, (a very long time for me), I enjoyed most of it. I did learn a few things about how forecasts work, and how to spot the difficulties in forecasting any given event. However it is more of a high-level look, and you will not learn much about how to actually forecast anything from this book. I would have preferred the book to either be more entertaining (Moneyball), or more "textbooky" rather than trying to be a little of both. In some ways, I think I would have enjoyed it more if I knew less about the math of statistics. Still, if you are at all interested in how complicated processes like weather, chess and national economies are forecast, it is well worth a read.

### ⭐⭐⭐⭐⭐ The beauty of Bayes applied to many domains
*by A***S on October 25, 2012*

This book is similar to Steven Levitt's Freakonomics: A Rogue Economist Explores the Hidden Side of Everything (P.S.) , Nassim Taleb's The Black Swan: Second Edition: The Impact of the Highly Improbable: With a new section: "On Robustness and Fragility" , and James Surowiecki's The Wisdom of Crowds . All four books explore the intersection of data, human behavior, and outcomes. They explain how to quantify outcomes within the financial markets, professional sports or elections. This book is especially interesting because Nate Silver has honed firsthand his statistical skills onto numerous domains including professional poker, baseball performance forecasting (he developed one of the best software program to do that), political elections (his "fivethirtyeight" blog). And, when he is not a firsthand practitioner he is a first class investigator. The first seven chapters cover the errors and successes people have had in forecasting in various disciplines. Chapter eight is the most pedagogical, as the author explains the basics of Bayes Theorem that he considers as an overall solution to many of the errors we make in forecasting. The last five chapters focus on Bayesian thinking within various disciplines. Nate Silver's coverage of the credit rating agencies "Catastrophic failure of prediction" (first chapter title) is excellent. In a single sentence on page 13, he captures the cause of the financial crisis: "In advance of the financial crisis, the system was so highly leveraged that a single lax assumption in the credit rating agencies played a huge role in bringing down the whole global financial system." Silver states that the AAA rated CDOs were deemed to have a default rate of only 0.12%. The actual default rate was 28% or over 200 times greater! This was because the rating agencies missed out the correlation between mortgage default rates at different locations when a nationwide home price downturn hit (see figure 1.2 on page 28. Watch out that he mislabeled column 3 and 4 from the right). Silver assesses that overall leverage was too high during the housing bubble. Fannie Mae and Freddie Mac had a debt-to-equity leverage of 70-to-1. Lehman Brothers and other investment banks were leveraged over 30-to-1. Borrowers had often loan-to-value ratios of 100% on their homes. The volume of credit default swaps, MBS, CDOs represented 30 to 60 times the volume of home sales during the bubble years (fig. 1.5 page 35). Nate Silver summarizes the errors made. Investors trusted the rating agencies. The rating agencies assumed home prices would never decline on a nationwide basis because they never had since the Great Depression. Lenders and borrowers believed rising home prices would bail them out through refinancing. Policymakers believed the financial system had enough capital and was self-disciplined. And, economists completely missed the ensuing severe recession. Nate Silver focuses next on political predictions. This field of experts was so bad at predicting it motivated him to enter it by starting his fivethirtyeight blog. He documents their failings extensively. Within this chapter he refers to the theory of Philip Tetlock, professor of psychology and political science at Berkeley. Tetlock had surveyed predictions of experts in various fields. And, he categorized them within two archetypes: the hedgehogs and the foxes. The hedgehogs are dogmatic, rarely change their minds, and are very confident of their forecast. The foxes are just the opposite. They update their forecasts as often as new information warrants it. As a result, they make better forecasts. The chapter on baseball is one of the best because of Silver's extensive firsthand experience. He uncovers many concepts applicable to many sports such as the age-curve of baseball performance (pg. 81). All sports have a predetermined age-curve. Actually, every single aspects of life including life itself have predetermined age-curves. His description of what it takes to be a successful professional baseball player (pg. 97) has also surprisingly broad applications. The conclusion of the chapter is also fascinating. It describes baseball management as a competitive arms race of intelligence gathering to extract small competitive edges. And, that those competitive edges are short-lived. That's a very interesting application of the Efficient Market Hypothesis. The chapter on economists documents how inaccurate their forecasts are. The majority can't forecast a recession that has already started as they missed out on the three most recent ones (1990, 2001, 2007). In November 2007, the average economic forecast was 2.4% real GDP growth in 2008. Instead, real GDP shrank by -3.3%. Economists assigned only a 1-in-2000 chance of the economy shrinking that much. Yet, home prices were already declining. Foreclosures had picked up. Bear Stearns had gone belly up six months ago. Those were powerful signals the housing and financial markets were on the edge of a cliff. Also, economists are way too confident. The few times you can extract confidence intervals from the economic profession they are invariably way too narrow because they underestimate the error level within their forecasts (pg. 182). Nate Silver states that: "this property of overconfident prediction has been observed also in medical research, political science, finance, and psychology" (pg. 183). Despite our having so much more data and computer power at our hands, economic forecasting has not improved since 1968. This is because our underlying understanding of cause and effects has not changed much since. Chapter 8 introduces Bayes's Theorem. Here Nate Silver often refers to a very good book on the subject: The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy by Sharon Bertsch McGrayne. Chapter 9 and 10 about chess and poker are excellent. Kasparov was ultimately beaten by a computer bug. IBM Big Blue made a move late in the last game that did not make any sense (the team who programmed it confirmed it was due to a small programming bug). Kasparov who was in a vulnerable position could not figure out that move and in despair resigned the game and lost the series. The Pareto principle of prediction on page 312 and 314 and the ensuing economics of poker are really interesting. Poker winning are heavily dependent on the one worst player at a table. If he leaves, the winnings are a lot harder to reap. Chapter 11 on the Efficient Market Hypothesis (EMH) is excellent. Nate Silver states that the stock market is efficient most of the time, although it is never perfectly efficient (that would preclude a market). But, it can be wildly inefficient on few occasions associated with bubbles and crashes. Nate Silver demonstrates how both technical analysis and fundamental analysis do not beat the market over the long run. Fig 11.3 on page 340 shows no correlation between the performance of mutual funds over the 2002 to 2006 period vs over the 2007 to 2011 period. Past performance is no guarantee of future returns. Next, Silver refers to Robert Shiller in showing the market is not as efficient as the EMH entails. Shiller looked at the P/E ratio of the S&P 500 over a trailing 10 year period and looked at prospective returns. And, the longer the period contemplated the greater the negative correlation between trailing P/E levels and future average yearly returns. This suggests that the market can get overvalued. But, the return correction is not apparent until looking at average return over a 10 to 20 year period. Next, Nate Silver refers to the works of Richard Thaler and Daniel Kahneman in behavioral economics to outline how market traders are not perfectly rational. They suffer from herd mentality, overconfidence, and being overly emotional rendering their trading pro-cyclical. So, if the market is not so efficient, can you beat it? Probably not. On page 345, Nate Silver demonstrates how a hypothetical investor with perfect timing over a decade (1976-1986) would get killed by very small transaction costs. Even though this investor would handily beat the stock market before transaction costs, he would wipe out most of his capital after transaction costs. Silver next tests a prudent investment strategy over the 1970 to 2009 period. He assumes an investor is prudent and sells his position in the S&P 500 index whenever it had declined 25% from its peak and reinvests whenever it recovered 90% of its value. Such an investor would have earned only 2.6% per year vs close to 10% for a simple buy-and-hold strategy. Nate Silver does believe several hedge funds can beat the market. But, they have intellectual and technological resources that no retail investor and few mutual funds can match. Chapter 12 on climate change is really interesting. He differentiates between where scientists agree and disagree. They all agree that the greenhouse effect exists and keeps the Earth warmer than it would otherwise be; that temperatures have risen over the past century; that greenhouse gases have contributed to that trend; and that water vapor is by far the most potent greenhouse gas (not CO2 as the Media conveys). The majority of scientists agree that rising CO2 concentration does contribute to rising temperature. But, there is a debate regarding how much. Where the scientific community is more divergent is regarding climate models and projections. They acknowledge that Al Gore's An Inconvenient Truth deterministic apocalyptic message was way off base. Nate Silver explains why there is much uncertainty regarding climate models' projections. One uncertainty is figuring out CO2 levels 100 years down the road. Another uncertainty is getting the causal relationships right (there is a lot more than CO2 at play). Another uncertainty concerns whether those models are programmed correctly. Within the vast quantities of computer codes, are there a few bugs that contribute to generating erroneous forecasts? Nate Silver reviews the prediction of the IPCC's 1990 model and observes that temperatures have not risen as fast as the model predicted. Current temperatures are below the model's 95% confidence interval. This lead the IPCC to reduce their baseline temperature increase from 3 degree Celsius per century in 1990 to 1.8 degree in 1995. On page 407, Silver comes up with an interesting application of Bayes theorem applied to rising temperature predictions. The last chapter on terrorism is intriguing. Terrorist attacks follow a similar Power Law as earthquakes. The frequency of events declines exponentially with increase in intensity. More violent events are much rarer than lesser ones. But, the few major events dominate the data in human casualties. For instance, 9/11 represented more than half of the total fatalities from terror attacks in NATO countries since 1979. Thus, it is worth exploring means of mitigating the impact of such events.

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