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The Signal and the Noise: Why So Many Predictions Fail - But Some Don't 1st Edition
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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.Read more ›
During election season, everyone with a newspaper column or TV show feels entitled to make (transparently partisan) predictions about the consequences of each candidate's election to unemployment/crime/abortion/etc. This kind of pundit chatter, as Silver notes, tends to be insanely inaccurate. But there are also some amazing success stories in the prediction business. I list some chapter-by-chapter takeaways below (though there's obviously a lot depth more to the book than I can fit into a list like this):
1. People have puzzled over prediction and uncertainty for centuries.
2. TV pundits make terrible predictions, no better than random guesses. They are rewarded for being entertaining, and not really penalized for being wrong.
3. Statistics has revolutionized baseball. But computer geeks have not replaced talent scouts altogether. They're working together in more interesting ways now.
4. Weather prediction has gotten lots better over the last fifty years, due to highly sophisticated, large-scale supercomputer modeling.
5. We have almost no ability to predict earthquakes. But we know that some regions are more earthquake prone, and that in a given region an earthquake of magnitude n happens about ten times as often as an earthquake of magnitude (n+1).
6. Economists are terrible at predicting quantities such as next year's GDP. Predictions are only very slightly correlated with reality.Read more ›
Returning to the election prediction issue, consider that each poll of 1000 people has a sampling error of +-5%, easily derived from Poisson statistics. However, when one pools the results from say 25 polls (and removes bias), the sample size is increased by 25-fold, which reduces the sampling error by 5-fold, down to +-1%. Thus, one can make confident predictions over differences FAR smaller than the usual sampling error. When one combines Bayesian pooling with a state-by-state analysis, one can make astonishingly accurate predictions ... Nate predicted ALL 50 states correctly, so his electoral count was exactly on reality as well when fractional electoral counts are eliminated.
Buy the book as it is educational and fun to read.
1. 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.
3. 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. "Bayes rule" is simply a mathematical gadget to combine these three pieces of information and output the prediction (the chance that the particular woman with a positive mammogram has cancer). There is a very detailed explanation of this online (search Google for "yudkowsky on bayes rule"), 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.Read more ›
Most Recent Customer Reviews
A detailed, methodological look at how to distinguish important data in a world overwhelmed with information - and how to use it. Read morePublished 3 days ago by Amazon Customer
This book helps to focus on right things when trying to predictPublished 5 days ago by Amazon Customer
Nate Silver is a name I knew from Baseball Prospectus, before he made it "big" with his political predictions. Read morePublished 16 days ago by Anono
finally, the end of the ridiculous, and itself insignificant. t-test...Published 17 days ago by Henry
Great concept. Would have liked him to dig deeper on some of the subject matter.Published 20 days ago by Chris Mo
I really enjoyed this book.m as a math geek, I was bound to. But I think even the non geeks can enjoy it. Read morePublished 23 days ago by Craig W. Reynolds
very interesting, especially as society progresses using data much more, this is going to rule our decisionsPublished 25 days ago by Michael H.
There are chapters on earth quakes, poker, baseball, stock market and climate. The math is not complicated and the book is fun read.Published 1 month ago by AchalAugustine