The signal and the noise: why most predictions fail but some don't / Nate Silver. The Signal and the Noise: Why So Many Predictions Fail--but Some Don't [Nate Silver] on mtn-i.info *FREE* shipping on qualifying offers. One of the more. Editorial Reviews. mtn-i.info Review. site Best Books of the Month, September People love statistics. Statistics, however, do not always love them.
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Get Instant Access to PDF File: #aeb The Signal And The Noise: Why So Many Predictions Fail But Some Don't By Nate Silver PDF. The Signal and the Noise: Why Most Predictions Fail – but Some Don't is a book by Nate . Create a book · Download as PDF · Printable version. Signal processing noise can be classified by its statistical properties Contaminated Gaussian noise, whose PDF is a linear mixture of Gaussian PDFs.
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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. The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.
In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success?
Are they good—or just lucky?
What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.
Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.
Read more Read less. Kindle Cloud Reader Read instantly in your browser. Customers who bought this item also bought. Page 1 of 1 Start over Page 1 of 1. The Art and Science of Prediction. The Big Sort: Bill Bishop. The Art of People: Dave Kerpen.
Command and Control: Eric Schlosser. Fooled by Randomness: Nassim Nicholas Taleb. Barking Up the Wrong Tree: Eric Barker. Editorial Reviews site. People love statistics. Statistics, however, do not always love them back. The Signal and the Noise , Nate Silver's brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Data meets human nature. Baseball, weather forecasting, earthquake prediction, economics, and polling: In all of these areas, Silver finds predictions gone bad thanks to biases, vested interests, and overconfidence.
But he also shows where sophisticated forecasters have gotten it right and occasionally been ignored to boot. In today's metrics-saturated world, Silver's book is a timely and readable reminder that statistics are only as good as the people who wield them.
Silver doesn't offer one comprehensive theory for what makes a good prediction in his interdisciplinary tour of forecasting. But the book is a useful gloss on the tricky business of making predictions correctly. See all Editorial Reviews.
Product details File Size: Penguin Books; 1 edition September 27, Publication Date: September 27, Sold by: English ASIN: BV65R54 Text-to-Speech: Enabled X-Ray: Computer Science. Book Series.
Is this feature helpful? Thank you for your feedback. Share your thoughts with other customers. Write a customer review. Customer images. See all customer images. 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. Climate scientist Michael E. Mann criticized the book for analyzing the "hard science" physical phenomena of climate trends with the same approach as used to analyze the social phenomena of voter preferences, which he characterized as "laden with subjective and untestable assumptions".
However, he purposefully leaves out the mathematics.
In , after his triumph of predicting the outcome of the last two presidential elections and selling his "fivethirtyeight" blog to the New York Times, Nate Silver accomplished what is almost impossible. In his recent book The Signal and the Noise, he correctly describes the discipline of making predictions, without explicitly invoking the math.
He accomplishes this feat even though the prediction methods he describes require more than one kind of mathematics.
By leaving out the math, he has reached a broad audience with a compelling book with lots of examples. It was published in Japanese in November 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.