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19 of 20 people found the following review helpful
5.0 out of 5 stars A Brief Summary and Review,
This review is from: The Signal and the Noise: Why So Many Predictions Fail-but Some Don't (Hardcover)*A full executive summary of this book is available at newbooksinbrief dot com.
Making decisions based on an assessment of future outcomes is a natural and inescapable part of the human condition. Indeed, as Nate Silver points out, "prediction is indispensable to our lives. Every time we choose a route to work, decide whether to go on a second date, or set money aside for a rainy day, we are making a forecast about how the future will proceed--and how our plans will affect the odds for a favorable outcome" (loc. 285). And over and above these private decisions, prognosticating does, of course, bleed over into the public realm; as indeed whole industries from weather forecasting, to sports betting, to financial investing are built on the premise that predictions of future outcomes are not only possible, but can be made reliable. As Silver points out, though, there is a wide discrepancy across industries and also between individuals regarding just how accurate these predictions are. In his new book `The Signal and the Noise: Why So Many Predictions Fail--but Some Don't' Silver attempts to get to the bottom of all of this prediction-making to uncover what separates the accurate from the misguided.
In doing so, the author first takes us on a journey through financial crashes, political elections, baseball games, weather reports, earthquakes, disease epidemics, sports bets, chess matches, poker tables, and the good ol' American economy, as we explore what goes into a well-made prediction and its opposite. The key teaching of this journey is that wise predictions come out of self-awareness, humility, and attention to detail: lack of self-awareness causes us to make predictions that tell us what we'd like to hear, rather than what is true (or most likely the case); lack of humility causes us to feel more certain than is warranted, leading us to rash decisions; and lack of attention to detail (in conjunction with self-serving bias and rashness) leads us to miss the key variables that make all the difference. Attention to detail is what we need to capture the signal in the noise (the key variable[s] in the sea of data and information that are integral in determining future outcomes), but without self-awareness and humility, we don't even stand a chance.
While self-awareness requires us to make an honest assessment of our particular biases, humility requires us to take a probabilistic approach to our predictions. Specifically, Silver advises a Bayesian approach. Bayes’ theorem has it that when it comes to making a prediction, the most prudent way to proceed is to first come up with an initial probability of a particular event occurring (rather than a black and white prediction of the form ‘I believe x will occur’). Next, we must continually adjust this initial probability as new information filters in.
The level of certainty that we can place on our initial estimate of the probability of a particular event (and the degree to which we can accurately refine it moving forward) is limited by the complexity of the field in which we are making our prediction, and also the amount and quality of the information that we have access to. For instance, in a field like baseball, where wins and losses mostly comes down to two variables (the skill of the pitchers, and the skill of the hitters), and where there is an enormous wealth of precise data, prediction is relatively straightforward (but still not easy). On the other hand, in a dynamic field such as the American economy, where the outcomes are influenced by an enormous number of variables, and where the interactions between these variables can become incredibly complex (due to things like positive and negative feedback), probabilities become a whole lot more difficult to pin down precisely (though they often remain possible on a general and/or long-term scale).
It is also important to recognize that while additional information can help us no matter what field we are trying to make our prediction in, we must be careful not to think that information can stand on its own. Indeed, additional information (when it is not met with insightful analysis) often does nothing more than draw our attention away from the key variables that truly make a difference. In other words, it creates more noise, which can make it more difficult to identify the signal. It is for this reason that predictive models that rely on statistics and statistics alone are often not very effective (though they do often help a seasoned expert who is able to apply insightful analysis to them).
In the final stage of the book Silver explores how the lessons that he lays out can be applied to such issues as global warming, terrorism and bubbles in financial markets. Unfortunately, each of these fields is a lot noisier than many of us would like to think (thus making them very difficult to predict precisely). Nevertheless, the author argues, within each there are certain signals that can help us make better predictions regarding them, and which should help make the world a safer and more livable place.
If you are hoping that this book will make you a fool-proof prognosticator, you are going to be disappointed. A key tenet of the book is that this is simply not possible (no matter what field you are in). That being said, Silver makes a very strong argument that by applying a few simple principles (and putting in a lot of hard work in identifying key variables) our predictive powers should take a great boost indeed. A full executive summary of this book is available at newbooksinbrief dot com; a podcast discussion of Silver's treatment of Bayes' theorem is also available.
9 of 11 people found the following review helpful
5.0 out of 5 stars I predict the people who should read this book, won't,
This review is from: The Signal and the Noise: Why So Many Predictions Fail-but Some Don't (Hardcover)You will hear a lot more about this book now that the author is becoming famous for his accurate prediction of the US election.
The book makes a compelling case for why we all need to learn more about statistics in order to better understand the world, and it also helps you learn at least the basics of what you need to know.
The first half explains why we often make enormous mistakes in our reasoning because we see patterns which aren't there - the apparent signal which is really an accident of the noise (there is no man in the moon - we just interpret random craters and flood plains that way). It makes a convincing case why we need to read on.
Once he has got us to understand how we make so many errors, he introduces us to the way out: Bayesian statistics.
I had tried before to get the idea behind Bayesian statistics but until I read this, I was not doing so well because I don't have the spare time to learn another whole branch of mathematics. By using many real world examples from sports and gambling, he leads us step-by-step, without equations, to understanding the basic principles and gave me the ability to see where I might be going wrong. You might need some more books before you can work the math yourself to come up with the kind of outstanding bets Nate Silver has made, as he tells us how he was able to beat the odds at betting on baseball thorough the use of statistical reasoning, but at least you'll know where you and others are going wrong and have some idea on how to start on better answers.
The title of this review is based on my sad realisation that the kind of people who want the simple answers that Silver demonstrates are not there are not the kind of people who read this type of book where better answers are to be found. The Canadian government famously claims "We don’t govern on the basis of statistics.". This book explains why that is the main basis on which they should govern.
There are a few claims in the book which I think are wrong. He occasionally wanders off into philosophical musings - for example claiming that Bayes Theorem somehow shows that Bayes theorem will become more widely accepted. No, it doesn't, if only because there are many people like those mentioned above who won't even bother to try to learn. However, the book certainly contains enough evidence that we would be better off if it does become the basis of more of our decisions and enough explanation that you will be much wiser in your decisions when you have finished.
1 of 1 people found the following review helpful
4.0 out of 5 stars Good examples, but the ideas are nothing too new,
This review is from: The Signal and the Noise: Why So Many Predictions Fail-but Some Don't (Hardcover)Silver's book provides great examples on real-world statistical patterns (or lack thereof), and how they often are not as simple (or comfortable) as they appear. Topics include poker, baseball, elections, terrorism, global warming, chess, hurricanes, and earthquakes, but the underlying ideas are covered in somewhat older (and more well-known) literature such as Taleb's 'The Black Swan'.
4.0 out of 5 stars Very interesting look at difficulties in distinguishing signal from noise.,
5.0 out of 5 stars a very informative and thorough book,
This review is from: The Signal and the Noise: Why So Many Predictions Fail-but Some Don't (Kindle Edition)This was a very worthwhile read. It replaced a significant part of my cynicism about the way data is often reported with a more scientific and thoughtful understanding
4.0 out of 5 stars Easy and Entertaining Read,
4.0 out of 5 stars Interesting,
This review is from: The Signal and the Noise: Why So Many Predictions Fail-but Some Don't (Kindle Edition)Well written and interesting, I thought it would have been boring since its mostly statistics and forecasting but it kept me interested right to the end with some useful insights
0 of 1 people found the following review helpful
5.0 out of 5 stars Great read,
0 of 4 people found the following review helpful
5.0 out of 5 stars Comment,
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The Signal and the Noise: Why So Many Predictions Fail-but Some Don't by Nate Silver (Hardcover - Oct. 2 2012)
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