THE SIGNAL AND THE NOISE: WHY SO MANY PREDICTIONS FAIL--BUT SOME DONT PDF, EPUB, EBOOK

Nate Silver | 560 pages | 03 Feb 2015 | Penguin Books | 9780143125082 | English | United States The Signal and the Noise: Why So Many Predictions Fail—But Some Don't by Nate Silver Please note that the tricks or techniques listed in this pdf are either fictional or claimed to work by its creator. We do not guarantee that these techniques will work for you. Some of the techniques listed in The Signal and the Noise: Why So Many Predictions Fail - But Some Dont may require a sound knowledge of Hypnosis, users are advised to either leave those sections or must have a basic understanding of the subject before practicing them. DMCA and Copyright : The book is not hosted on our servers, to remove the file please contact the source url. If you see a Google Drive link instead of source url, means that the file witch you will get after approval is just a summary of original book or the file has been already removed. Loved each and every part of this book. I will definitely recommend this book to non fiction, science lovers. Your Rating:. Your Comment:. As a devotee of , website and especially podcasts, I was looking forward to this book. I wanted to love it. It was enjoyable, certainly, and I learned a few things. But after opening the door, it Nate Silver. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the election. 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. 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. The Signal and the Noise: Why So Many Predictions Fail-but Some Don't - Nate Silver - Google книги

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. 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. View all 15 comments. Jan 01, Ted rated it it was amazing Shelves: math , americana. Presidential elections. I was following the writing on the site right up to the night of the election. And on election day, the article which pointed out early signs that Hillary could be in trouble was so accurate that I had given up for her before 10 pm that evening. And, despite any negative impressions I may leave below about any issues I previously had with Silver's writing, or his style, the last few years, in which he's developed his own web site, together with the interactions he's had will the commenters and other statisticians that he's hired, have made his writing a model of clearness and conciseness. He also nowadays is very careful to refrain from making rash statements about probabilities, usually listing many reasons why the "odds" being quoted could be risky bets. Anyway - before Silver's election triumphs he was known to a less wide, but no less fervid, audience as a sabermetrician who, starting in , contributed predicted statistical ranges of performance for major league baseball players to the Baseball Prospectus. In The Signal and the Noise , Silver discusses issues related to these foundations of his reputation in the second and third chapters. On balance I found the book, in terms of insights offered and simple interest, much closer to the political chapter than the baseball chapter — thus the high rating. This impressed me as an attempt possibly at the urging of an editor? To be fair, Silver does have a habit of putting comments in addition to source information in his footnotes. Where I believe he often errs is in not needing a source for a statement that is pretty non-controversial; in these cases the comment could just be inserted into the text and the footnote dispensed with. But Big Data is only briefly mentioned in the book, and is brought up again in the Conclusion in a correspondingly unenlightening manner. The difficulty in handling large amounts of data is separating the signal from the noise. The theme, expressed in this manner, is handled more or less brilliantly throughout. Once past the Introduction, the book immediately improved. Silver seemed to quickly find his comfort level in treating one area after another in which we attempt to make predictions, with varying success. The great majority of the chapters I found very interesting. Silver writes well, and can clearly get across his points. He shows convincingly I think how these fields differ from one another, and how the problems they have with making successful predictions and forecasts vary from field to field, depending on a variety of elements. I approached the chapter on climate prediction with some trepidation, wondering if Silver was going to somehow take the position that it was all baloney. So he feels there is a case to be made for some skepticism regarding the accuracy of the models, and thus of the forecasts being produced by the models. Most of us realize that because of the catastrophic consequences of these very unlikely events, buying insurance is rational. That is his interest in, and application of, Bayesian reasoning or inference. Silver is quite obviously much taken with this, and he does a good job in my opinion of explaining it. In almost every chapter following this he refers to the way that Bayesian reasoning can be used to strengthen forecasting and to overcome some of the difficulties of predicting in that area. View all 13 comments. Apr 18, David rated it it was amazing Shelves: economics , geology , science , environment , mathematics , meteorology. 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 This is a fantastic book about predictions. 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. View all 3 comments. Mar 16, Julie rated it really liked it Shelves: overdrive , , e-book , non-fiction. More Information, more problems- This book was recommended by one the many books related emails I get each day. Nevertheless, I must have thought it sounded interesting and placed a hold on it at the library. Many of you may be familiar with statistician, Nate Silver. I admit I was not familiar with his work until now. However, after reading this book, I think I will keep a closer eye on his website. This book examines the way data is analyzed, how some predictions are correct and why some fail. The noise is what distracts us from the truth. With the polls and the media thinking they had the most recent election forecasted, I think people are warier than ever. That may be why there has been a renewed interest in this book. The first section of the book, takes a look at the various ways experts make predictions, and how they could miss something like the financial crisis, for example. Silver does speak to political predictions. The second portion of the book is where Silver really excels: Baseball statistics. Now, this section really appeals to baseball fans, which I am not. But, it also would appeal to those who understand math and complicated Algorithms. Again, not my thing. I tried my best to understand this section, but just could not get into it and because it was not a topic I was well versed in, much of it went over my head and frankly, it was boring to me. So, I gave up on this section and went to the next. Weather: This section, which deals with prediction of major weather events, such as hurricanes was very interesting. Weather forecasting not only has an effect on safety, but on our economy as well. Many times, forecasters get things right, and many lives are saved, but at times, they get in right, but things are not as bad as predicted, such as the recent blizzard expected to hit NYC. Yet, as frustrating as that may be, erring on the side caution, still might be a good thing, and remember, many weather forecasters, those working behind the scenes, are not being paid exorbitant fees. Just think about the times when you made it out of the path of a tornado, and be thankful for these guys, who must decipher an incredible amount of data and unpredictable patterns, and they must deal with the human element on top of that. But, there has to be an honesty in forecasting, too. Television ratings can come into play, too, unfortunately. This was my favorite section of the book. But, I did find the book fascinating, informative, and chock full calculations juxtaposed against unpredictable elements that could not be foreseen, or against patterns in plain sight, were ignored, all mix together to prove why predictions and forecast often fail, but also, what makes them work! Sep 30, Ilya rated it it was ok. This book was a disappointment for me, and I feel that the time I spent reading it has been mostly wasted. I will first, however, describe what I thought is good about the book. Everything in this book is very clear and understandable. As for the content, I think that the idea of Baysean thinking is interesting and sound. The idea is that, whenever making any hypothesis e. The general prevalence of breast cancer in population. This is often called the "prior": how likely did you think it was that the woman had cancer before you saw the mammogram 2. The chance of getting a positive mammogram for a woman with cancer. The chance of getting a positive mammogram for a woman without cancer. People often tend to ignore items 1 and 3 on the list, leading to very erroneous conclusions. There is a very detailed explanation of this online , no worse if more technical than the one in the book. If you'd like a less technical description, read chapter 8 of the book but ignore the rest of it. Now for the bad. While the Baysean idea is valuable, its description would fit in a dozen of pages, and it is certainly insufficient by itself to make good predictions about the real world. I had hoped that the book would draw on the author's experience and give an insight into how to apply this idea in the real world. It does the former, but not he latter. There are lots of examples and stories sometimes amusing; I liked the Chess story in Chapter 9 , but the stories lead the reader to few insights. The examples only lead to one conclusion clearly. If you need to be convinced that "the art of making predictions is important, but it is easy to get wrong", read this book. If you wonder: "how can we actually make good predictions? The only answers provided are useless platitudes: for example, "it would be foolish to ignore the commonly accepted opinion of the community, but one must also be careful to not get carried away by herd mentality". While I was searching for the words to describe the book, I have found the perfect description in Chapter 12 the book itself: Heuristics like Occam's razor An admonition like "The more complex you make the model the worse the forecast gets" is equivalent to saying "Never add too much salt to the recipe" If you want to get good at forecasting, you'll need to immerse yourself in the craft and trust your own taste-buds. Had this quote been from the introduction, and had the book given any insight into how to get beyond the platitudes, it would be the book I hoped to read. However, the quote is from the penultimate chapter, and there is no further insight inside this book. View all 6 comments. Jul 19, Kate rated it it was amazing Shelves: read-it-on-an-airplane , data-porn. I'm going to do this the Nate Silver Bayesian way. Kind of. New Event -- I read Nate Silver's book Probability that I will fly to New York and track him down and thrust a drink in his hand because this was a great book and I am impressed. Feel free to check my math. View 2 comments. Dec 17, Olive Fellows abookolive rated it liked it. I was expecting a lot of data but this was Oct 25, Dewey rated it did not like it. I wanted to like this book as I enjoy reading Silver's blog. The majority of chapters in this book are inferior rehashes of arguments and anecdotes from other authors. The book is clearly intended to capitalize on the popularity of his blog, which as John Cassidy of the New Yorker just articulated overemphasizes the use of Monte-Carlo simulations to come up with inanely precise projections of a te I wanted to like this book as I enjoy reading Silver's blog. The book is clearly intended to capitalize on the popularity of his blog, which as John Cassidy of the New Yorker just articulated overemphasizes the use of Monte-Carlo simulations to come up with inanely precise projections of a tenth of a point of who will win the Presidential election. While heuristics and Monte-Carlo style simulations may provide details given the parameters included in the model; Silver's assumptions about the usefullness of one poll over another; and the averaging of prediction markets generally reach similar conclusions to what basic common sense would dictate. I happen to believe just as some people inevitably beat the market by looking at past historical data without actual acumen, Silver's model seems to have been successful. The self-aggrandizing by Silver of his own skill at Poker, political forecasting, sports betting etc, seems to belie his own understanding of Bayesian theory and at times reach nauseating levels. I don't care to know his own personal income from limit poker or his player tracking system used by baseball prospectus. The books dabbles in many areas and is truly compelling in none of them. While not an awful book, a curious reader would be better served by reading separate books on area's of interest including book's that offer a stronger statistical background and less "pop culture" examples. I do not recommend this book to anyone. See more Timeisrhythm. Feb 13, Wen rated it it was amazing Shelves: data-science. Another classic on statistics. This one focused more on real-life applications; sports, politics, finance, weather, climate change I assume those who had basic statistics would enjoy it more. All easy say or read than do : Here is my prediction Le Another classic on statistics. This was my second read of the book as part of my recent series of refreshers on statistics and data analysis. I felt I appreciated Silver's approach to the problems more this time, hence I added one star. In general, it was an interesting and insightful read, although I have mixed feelings about some of the chapters and concepts, and sometimes the pretentious tone of presenting ideas. Let's start by two weaknesses: At some points it seems good prediction looks like a 'hammer' to see all the problems as 'needles'. So, all the problems can be interpreted as the failures of prediction. To me it does not sound very scientific in a Popperian sense : an 'out-of-sample' situation for Silver is close to what Talib uses to explain 'antifragility'. Or the concepts of hedgehogs and foxes are interesting, but the implications are black and white, in a gray word. Furthermore, there is too much detail and bla-blas on some of the topic such as baseball and basketball players in America, which makes the book boring or too Americanized! However, it tries to highlight the importance of statistics, and the way facts less quantifiable and accessible for everyone contribute to unique predictions. The second and the more analytical half of the book was more interesting to me. 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 I followed Nate Silver's blog FiveThirtyEight closely during the run-up to election day 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. Your predictions should approach reality as you continually refine them. Without any really bad players at the table, it's nearly impossible for anyone but the top players to turn a profit. 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. Mar 27, Mehrsa rated it liked it. Some interesting parts, but it's really hard to take this superforecaster seriously on political forecasting--you know what I mean? And I am sort of over the moneyball theory too. I mean, it was useful a few years ago to break free from "gut feelings", but I think the pendulum swung too far into just cold data and needs to swing back into the world of humans and fat tails and Trump getting elected. 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 This is a really amazing book - a must read for anyone who makes decisions or judgement calls. These different topics illustrate different statistical principles. For example, weather forecasting is used to introduce the idea of "calibration," or how well weather forecasts fit actual weather outcomes. There is much on the need for improved expressions of uncertainty in all statistical statements, reflecting ranges of probable outcomes and not just single "point estimates" like averages. The shares of the popular vote similarly are ranges including outcomes in which Romney gets the most votes. What is highly probable is that the voting shares are in these ranges, but not whose share is highest; that's another probability question with closer odds. From such information, it's up to the consumer of such statements to use that information as best they can in dealing with an uncertain future in an age of information overload. That last idea frames Silver's entire narrative and motivates his pedagogical mission. Silver rejects much ideology taught with statistical method in colleges and universities today, specifically the "frequentist" approach of Ronald Fisher , originator of many classical statistical tests and methods. The problem Silver finds is a belief in perfect experimental, survey, or other designs, when data often comes from a variety of sources and idealized modeling assumptions rarely hold true. Often such models reduce complex questions to overly simple "hypothesis tests" using arbitrary "significance levels" to "accept or reject" a single parameter value. In contrast, the practical statistician first needs a sound understanding of how baseball, poker, elections or other uncertain processes work, what measures are reliable and which not, what scales of aggregation are useful, and then to utilize the statistical tool kit as well as possible. Silver believes in the need for extensive data sets, preferably collected over long periods of time, from which one can then use statistical techniques to incrementally change probabilities up or down relative to prior data. This "Bayesian" approach is named for the 18th century minister Thomas Bayes who discovered a simple formula for updating probabilities using new data. For Silver, the well-known method needs revitalizing as a broader paradigm for thinking about uncertainty, founded on learning and understanding gained incrementally, rather than through any single set of observations or an ideal model summarized by just a few key parameters. Part of that learning is the informal process of changing assumptions or the modeling approach, in the spirit of a craft whose goal is to devise the best betting odds on well-defined future events and their outcomes. The Signal and the Noise - Wikipedia

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. 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. It dropped to No. The Signal and the Noise print edition was named Amazon's No. Arte e scienza della previsione, appeared in October It was published in Japanese in November A Korean language edition was published by The Quest in July A Polish edition was scheduled for publication in hardcover in by Helion : Sygnal i szum: Sztuka prognozowania w erze technologii. The book emphasizes Silver's skill, which is the practical art of mathematical model building using probability and statistics. Silver takes a big- picture approach to using statistical tools, combining sources of unique data e. The book includes richly detailed case studies from baseball, elections, climate change , the financial crash, poker, and weather forecasting. These different topics illustrate different statistical principles. For example, weather forecasting is used to introduce the idea of "calibration," or how well weather forecasts fit actual weather outcomes. There is much on the need for improved expressions of uncertainty in all statistical statements, reflecting ranges of probable outcomes and not just single "point estimates" like averages. The shares of the popular vote similarly are ranges including outcomes in which Romney gets the most votes. What is highly probable is that the voting shares are in these ranges, but not whose share is highest; that's another probability question with closer odds. From such information, it's up to the consumer of such statements to use that information as best they can in dealing with an uncertain future in an age of information overload. That last idea frames Silver's entire narrative and motivates his pedagogical mission. Silver rejects much ideology taught with statistical method in colleges and universities today, specifically the "frequentist" approach of Ronald Fisher , originator of many classical statistical tests and methods. The problem Silver finds is a belief in perfect experimental, survey, or other designs, when data often comes from a variety of sources and idealized modeling assumptions rarely hold true. Often such models reduce complex questions to overly simple "hypothesis tests" using arbitrary "significance levels" to "accept or reject" a single parameter value. In contrast, the practical statistician first needs a sound understanding of how baseball, poker, elections or other uncertain processes work, what measures are reliable and which not, what scales of aggregation are useful, and then to utilize the statistical tool kit as well as possible. Silver believes in the need for extensive data sets, preferably collected over long periods of time, from which one can then use statistical techniques to incrementally change probabilities up or down relative to prior data. This "Bayesian" approach is named for the 18th century minister Thomas Bayes who discovered a simple formula for updating probabilities using new data. For Silver, the well-known method needs revitalizing as a broader paradigm for thinking about uncertainty, founded on learning and understanding gained incrementally, rather than through any single set of observations or an ideal model summarized by just a few key parameters.

Your Comment:. Trumble by J. Add a review Your Rating: Your Comment:. Dont Let Me Go by J. White Noise by Don DeLillo. March: Book One by John Lewis. View all 13 comments. Apr 18, David rated it it was amazing Shelves: economics , geology , science , environment , mathematics , meteorology. 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 This is a fantastic book about predictions. 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. View all 3 comments. Mar 16, Julie rated it really liked it Shelves: overdrive , , e-book , non-fiction. More Information, more problems- This book was recommended by one the many books related emails I get each day. Nevertheless, I must have thought it sounded interesting and placed a hold on it at the library. Many of you may be familiar with statistician, Nate Silver. I admit I was not familiar with his work until now. However, after reading this book, I think I will keep a closer eye on his website. This book examines the way data is analyzed, how some predictions are correct and why some fail. The noise is what distracts us from the truth. With the polls and the media thinking they had the most recent election forecasted, I think people are warier than ever. That may be why there has been a renewed interest in this book. The first section of the book, takes a look at the various ways experts make predictions, and how they could miss something like the financial crisis, for example. Silver does speak to political predictions. The second portion of the book is where Silver really excels: Baseball statistics. Now, this section really appeals to baseball fans, which I am not. But, it also would appeal to those who understand math and complicated Algorithms. Again, not my thing. I tried my best to understand this section, but just could not get into it and because it was not a topic I was well versed in, much of it went over my head and frankly, it was boring to me. So, I gave up on this section and went to the next. Weather: This section, which deals with prediction of major weather events, such as hurricanes was very interesting. Weather forecasting not only has an effect on safety, but on our economy as well. Many times, forecasters get things right, and many lives are saved, but at times, they get in right, but things are not as bad as predicted, such as the recent blizzard expected to hit NYC. Yet, as frustrating as that may be, erring on the side caution, still might be a good thing, and remember, many weather forecasters, those working behind the scenes, are not being paid exorbitant fees. Just think about the times when you made it out of the path of a tornado, and be thankful for these guys, who must decipher an incredible amount of data and unpredictable patterns, and they must deal with the human element on top of that. But, there has to be an honesty in forecasting, too. Television ratings can come into play, too, unfortunately. This was my favorite section of the book. But, I did find the book fascinating, informative, and chock full calculations juxtaposed against unpredictable elements that could not be foreseen, or against patterns in plain sight, were ignored, all mix together to prove why predictions and forecast often fail, but also, what makes them work! Sep 30, Ilya rated it it was ok. This book was a disappointment for me, and I feel that the time I spent reading it has been mostly wasted. I will first, however, describe what I thought is good about the book. Everything in this book is very clear and understandable. As for the content, I think that the idea of Baysean thinking is interesting and sound. The idea is that, whenever making any hypothesis e. The general prevalence of breast cancer in population. This is often called the "prior": how likely did you think it was that the woman had cancer before you saw the mammogram 2. The chance of getting a positive mammogram for a woman with cancer. The chance of getting a positive mammogram for a woman without cancer. People often tend to ignore items 1 and 3 on the list, leading to very erroneous conclusions. There is a very detailed explanation of this online , no worse if more technical than the one in the book. If you'd like a less technical description, read chapter 8 of the book but ignore the rest of it. Now for the bad. While the Baysean idea is valuable, its description would fit in a dozen of pages, and it is certainly insufficient by itself to make good predictions about the real world. I had hoped that the book would draw on the author's experience and give an insight into how to apply this idea in the real world. It does the former, but not he latter. There are lots of examples and stories sometimes amusing; I liked the Chess story in Chapter 9 , but the stories lead the reader to few insights. The examples only lead to one conclusion clearly. If you need to be convinced that "the art of making predictions is important, but it is easy to get wrong", read this book. If you wonder: "how can we actually make good predictions? The only answers provided are useless platitudes: for example, "it would be foolish to ignore the commonly accepted opinion of the community, but one must also be careful to not get carried away by herd mentality". While I was searching for the words to describe the book, I have found the perfect description in Chapter 12 the book itself: Heuristics like Occam's razor An admonition like "The more complex you make the model the worse the forecast gets" is equivalent to saying "Never add too much salt to the recipe" If you want to get good at forecasting, you'll need to immerse yourself in the craft and trust your own taste-buds. Had this quote been from the introduction, and had the book given any insight into how to get beyond the platitudes, it would be the book I hoped to read. However, the quote is from the penultimate chapter, and there is no further insight inside this book. View all 6 comments. Jul 19, Kate rated it it was amazing Shelves: read-it-on-an-airplane , data-porn. I'm going to do this the Nate Silver Bayesian way. Kind of. New Event -- I read Nate Silver's book Probability that I will fly to New York and track him down and thrust a drink in his hand because this was a great book and I am impressed. Feel free to check my math. View 2 comments. Dec 17, Olive Fellows abookolive rated it liked it. I was expecting a lot of data but this was Oct 25, Dewey rated it did not like it. I wanted to like this book as I enjoy reading Silver's blog. The majority of chapters in this book are inferior rehashes of arguments and anecdotes from other authors. The book is clearly intended to capitalize on the popularity of his blog, which as John Cassidy of the New Yorker just articulated overemphasizes the use of Monte-Carlo simulations to come up with inanely precise projections of a te I wanted to like this book as I enjoy reading Silver's blog. The book is clearly intended to capitalize on the popularity of his blog, which as John Cassidy of the New Yorker just articulated overemphasizes the use of Monte-Carlo simulations to come up with inanely precise projections of a tenth of a point of who will win the Presidential election. While heuristics and Monte-Carlo style simulations may provide details given the parameters included in the model; Silver's assumptions about the usefullness of one poll over another; and the averaging of prediction markets generally reach similar conclusions to what basic common sense would dictate. I happen to believe just as some people inevitably beat the market by looking at past historical data without actual acumen, Silver's model seems to have been successful. The self-aggrandizing by Silver of his own skill at Poker, political forecasting, sports betting etc, seems to belie his own understanding of Bayesian theory and at times reach nauseating levels. I don't care to know his own personal income from limit poker or his player tracking system used by baseball prospectus. The books dabbles in many areas and is truly compelling in none of them. While not an awful book, a curious reader would be better served by reading separate books on area's of interest including book's that offer a stronger statistical background and less "pop culture" examples. I do not recommend this book to anyone. See more Timeisrhythm. Feb 13, Wen rated it it was amazing Shelves: data-science. Another classic on statistics. This one focused more on real-life applications; sports, politics, finance, weather, climate change I assume those who had basic statistics would enjoy it more. All easy say or read than do : Here is my prediction Le Another classic on statistics. This was my second read of the book as part of my recent series of refreshers on statistics and data analysis. I felt I appreciated Silver's approach to the problems more this time, hence I added one star. In general, it was an interesting and insightful read, although I have mixed feelings about some of the chapters and concepts, and sometimes the pretentious tone of presenting ideas. Let's start by two weaknesses: At some points it seems good prediction looks like a 'hammer' to see all the problems as 'needles'. So, all the problems can be interpreted as the failures of prediction. To me it does not sound very scientific in a Popperian sense : an 'out-of-sample' situation for Silver is close to what Talib uses to explain 'antifragility'. Or the concepts of hedgehogs and foxes are interesting, but the implications are black and white, in a gray word. Furthermore, there is too much detail and bla-blas on some of the topic such as baseball and basketball players in America, which makes the book boring or too Americanized! However, it tries to highlight the importance of statistics, and the way facts less quantifiable and accessible for everyone contribute to unique predictions. The second and the more analytical half of the book was more interesting to me. 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 I followed Nate Silver's blog FiveThirtyEight closely during the run-up to election day 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. Your predictions should approach reality as you continually refine them. Without any really bad players at the table, it's nearly impossible for anyone but the top players to turn a profit. 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. Mar 27, Mehrsa rated it liked it. Some interesting parts, but it's really hard to take this superforecaster seriously on political forecasting--you know what I mean? And I am sort of over the moneyball theory too. I mean, it was useful a few years ago to break free from "gut feelings", but I think the pendulum swung too far into just cold data and needs to swing back into the world of humans and fat tails and Trump getting elected. 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 This is a really amazing book - a must read for anyone who makes decisions or judgement calls. There are plenty of footnotes relevant to the page , but I didn't bother with the references at the back. All up it was not at all the onerous read I was expecting from the size and nature of the book. What I particularly liked was that it agrees with many of my "hunches" and "gut feels" that seem to work out mostly but more importantly puts theory that I can put to the tests and use more widely. A few points raised really made me feel chuffed and not alone a little cleverer than most : The misuse and misapplication of Occam's razor; Overfit of models onto data; Fisherian statistical significance particularly in medical science. There was only one "low" point; chapter 11 on free markets, "If you can't beat'em It started out as a slightly irked, though legitimate, response to a smart ass comment about a free market betting pool being a better predictor than his website. It then went into stock market trading and but didn't go far enough into the information inequalities with market making for my liking. The end conclusion two streams - indexed investment on signal trading and short trading on the noise , I agree with. A final point on my bad predictions: of the last 4 books I have read I have judged reading time and effort on size and been wrong 3 times - twice with small novels that were philosophically challenging and unpleasant to read and once with this behemoth of a book that was breeze to read! Jul 07, Laura Noggle rated it liked it Shelves: , nonfiction. Meh, I was hoping for more. Interesting at points, but the main message gets swallowed by the noise—almost too much random content. Basically, it's hard to predict stuff. Be careful what predictions you trust, most of them will be wrong a good portion of the time. The end. Nov 29, Mal Warwick rated it it was amazing Shelves: nonfiction. This is the guy who writes the FiveThirtyEight. And, by the way: Silver is just 34 years old as I write this post. As you might expect from this gifted enfant terrible, the book is as ambitious as it is digestible. Written in an easy, conversational style, The Signal and the Noise explores the ins and outs of predicting outcomes not just in politics, poker, and sports baseball and basketball as well as the stock market, the economy, and the financial meltdown, weather forecasting, earthquakes, epidemic disease, chess, climate change, and terrorism. As there is an exponential increase in the amount of available information, there is likewise an exponential increase in the number of hypotheses to investigate. But the number of meaningful relationships in the data. Most of the data is just noise, as most of the universe is filled with empty space. It concluded that most of these findings were likely to fail when applied in the real world. They could not replicate about two-thirds of the positive findings claimed in medical journals when they attempted the experiments themselves. We focus on those signals that tell a story about the world as we would like it to be, not how it really is. We ignore the risks that are hardest to measure, even when they pose the greatest threats to our well- being. We make approximations and assumptions about the world that are much cruder than we realize. We abhor uncertainty, even when it is an irreducible part of the problem we are trying to solve. Hedgehogs traffic in Big Ideas and often hew to ideologies; these are the people who talk to the press and are frequently found on TV talk shows. Foxes are cautious types who carefully examine and weigh details before reaching conclusions. Be very afraid. View 1 comment. Dec 27, Rick Presley rated it liked it. Nate Silver does an excellent job demonstrating the different domains where statistics plays a part. More importantly, he describes why methods that proved successful in one domain are inadequate or inappropriate to another domain. The best part about the book is that he doesn't resort to math to explain these differences. The problem with the book is that he fails to take the lessons from previous chapters and apply them to subsequent chapters. I think this may have explained his hubris in mis- Nate Silver does an excellent job demonstrating the different domains where statistics plays a part. I think this may have explained his hubris in mis-forecasting the election outcome. I did hear an interview with him that said his stats weren't wrong. If 2 out of 3 scenarios had Hillary winning, then 1 out of 3 scenarios had Trump winning. I think this illustrates his discussion on the difference between likelihood and probability. I would recommend this as a primer on stats for the non-mathematician, but I would caution that there are sprawling passages of boring stuff that you'll want to skip over. Sep 29, Brian Clegg rated it really liked it. It was really interesting coming to this book soon after reading The Black Swan, as in some ways they cover similar ground — but take a very different approach. I ought to say straight away that this book is too long at a wrist-busting pages, but on the whole it is much better than its rival. Where Black Swan is written in a highly self-indulgent fashion, telling us far too much about the author and really only containing one significant piece of information, Signal and Noise has much more c It was really interesting coming to this book soon after reading The Black Swan, as in some ways they cover similar ground — but take a very different approach. 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. https://files8.webydo.com/9585983/UploadedFiles/3BE223B0-8EA1-D90D-E40D-1F3F73F1297C.pdf https://files8.webydo.com/9588056/UploadedFiles/7CE44E1E-FC98-86A3-C1AB-D2F10735937B.pdf https://files8.webydo.com/9585910/UploadedFiles/3491F1B2-B676-D22A-9568-A5E8437953FE.pdf https://files8.webydo.com/9586824/UploadedFiles/320B584A-9A43-75EA-027D-8E0E33051A5D.pdf https://files8.webydo.com/9593079/UploadedFiles/AA8B4FA1-F349-192F-D255-070AA55DE99C.pdf