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The Signal and the Noise: The Art and Science of Prediction Paperback – 18 Apr 2013
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Outstanding... I was hooked (Tim Harford Financial Times)
One of the more momentous books of the decade (The New York Times Book Review)
A lucid explanation of how to think probabilistically (Guardian)
The inhabitants of Westminster are speed-reading The Signal and the Noise... They will find the book remarkable and rewarding (Sunday Telegraph)
Is there anything now that Nate Silver could tell us that we wouldn't believe? (Jonathan Freedland)
Fascinating... our age's Brunel (Bryan Appleyard Sunday Times)
A surprisingly accessible peek into the world of mathematical probability (Daily Telegraph)
The Galileo of number crunchers (Independent)
A 34-year old Delphic Oracle (Daily Beast)
About the Author
Nate Silver is a statistician and political forecaster at The New York Times. In 2012, he correctly predicted the outcome of 50 out of 50 states during the US presidential election, trumping the professional pollsters and pundits. He was named one of TIME's 100 Most Influential People in the world, and one of Rolling Stones' top Agents of Change. He lives in Brooklyn, New York.
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Top customer reviews
The Nobel prize-winning physicist Niels Bohr famously said 'Prediction is very difficult, especially if it's about the future'. This pretty much sums up the first half of the book. Yes, the detail about the financial crisis, weather forecasting, earthquakes etc is mildly interesting, but in relation to prediction, you will be wading through a lot of noise to extract the signal ('human nature makes us over-confident predictors', 'without either good theory or good empirical data, you may as well just guess','the most confident pundits are usually the worst' etc).
The substance of the book comes in twenty pages in the middle, where Silver introduces Bayesian logic (I learnt in maths classes at school when I was fourteen so it wasn't new to me, and it doesn't need 200 pages of build up). The best section is where Silver contrasts Bayesian logic to Fisherian logic. Fisher created the maths that is used almost universally in medical and social science research to prove the efficacy of a treatment or theory. Silver explains how flawed this maths is - which is presumably why two thirds of the positive findings claimed in medical journals cannot be replicated. This is pretty heady stuff.
Silver claims that the second half of the book is about how to make predictions better. It is mostly more examples of failure, this time in chess, investment, climate and terrorism, with a few asides that might be considered signals ('testing is good', 'groups/markets tend to make better predictions than individuals'). The exception is the section on poker, which delivers the strongest message in the book: good gamblers think in probabilities (rather than dead certs) - when these probabilities diverge from the odds on offer by a suitable margin, they may place a bet. Bad poker players lose a lot more than good poker players make. The best is the enemy of the good...
Of course, the point of the book is that there is no silver bullet - good prediction requires detail, nuance, hard work, honesty and humility. It would be wrong to expect a check list for success at the end, and naturally, there isn't one. Even so, you are left with a craving for clarity.
'The Signal and the Noise' is a pleasant enough read, but it is mostly anecdote. Rather ironically, you are left to sort out the signal from noise yourself.
The use of mathematical models in epidemiology is also portrayed in a misleading way: no-one would ever expect a simple SIR model to produce useful predictions about disease spread in a real-world population, rather these simple models are used in an exploratory way to help us understand the theoretically possible behaviours of such diseases (something which is mentioned at the end of the chapter as a bit of an afterthought). It would be better if this chapter looked at some examples where modelling has been useful in the management of disease, such as the fairly dramatic story of how models of foot-and-mouth disease spread in the UK were used to persuade the government to change policy and bring the army in to help deal with the disease during the 2001 outbreak.
A final comment is that the portrayal of Bayesian statistics as the only way to analyse data and the use of straw-man arguments to ridicule "frequentist" statistics and bash Fisher is getting really tired. I use both Bayesian and "frequentist" stats in my research and I am able to understand that both are useful under different circumstances and both have advantages and disadvantages. I suspect that if Silver had some experience with working in experimental science, for example, he would have a better appreciation for when conventional statistical analysis is a useful tool.
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