The Signal and the Noise: Why So Many Predictions Fail - but Some Don't
| Author | : | |
| Rating | : | 4.95 (691 Votes) |
| Asin | : | B009HL6444 |
| Format Type | : | |
| Number of Pages | : | 520 Pages |
| Publish Date | : | 2017-05-24 |
| Language | : | English |
DESCRIPTION:
Much-needed insight to understand and improve predictive science Sitting in Seattle 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 f. "Statistics in Context + Important Key Takeaways" according to J. Duncan. The author Nate Silver does a great job weaving more technical statistical concepts in context early in the book, so as not to lose readers early on. However I thought this would lead to more a detailed technical discussion later on, which the author said it would, but it never really transpired. Instead he kept to analogies and keeping the science of prediction in context. Which there's really nothing wrong with, if you're someone looking for that just not exactly what I wanted. Great book, and here are some takeaways Excellent book!!! People looking for a "how to predict" silver bullet will (like some reviewers here) be disappointed, mainly because Silver is too honest to pretend that such a thing exists. The anecdotes and exposition are fantastic, and I wish we could make this book required reading for, say, everyone in the country.During election season, everyone with a newspaper column or TV show feels entitled to make (transparently partisan) predictions about the consequences of each can
They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. If our appreciation of uncertainty improves, our predictions can get better too. But overconfidence is often the reason for failure. Both experts and laypeople mistake more confident predictions for more accurate ones. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. This is the "prediction paradox": The more humil