The Signal and the Noise
The Art and Science of Prediction
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5.0 • 1 Rating
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- €10.99
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- €10.99
Publisher Description
The International Bestseller by 'The Galileo of number crunchers' (Independent)
Every time we choose a route to work, decide whether to go on a second date, or set aside money for a rainy day, we are making a prediction about the future. Yet from the financial crisis to ecological disasters, we routinely fail to foresee hugely significant events, often at great cost to society. The rise of 'big data' has the potential to help us predict the future, yet much of it is misleading, useless or distracting.
In The Signal and the Noise, the New York Times political forecaster Nate Silver, who accurately predicted the results of every state in the 2012 US election, reveals how we can all develop better foresight in an uncertain world. From the stock market to the poker table, from earthquakes to the economy, he takes us on an enthralling insider's tour of the high-stakes world of forecasting, showing how we can all learn to detect the true signals amid a noise of data.
'Remarkable and rewarding' Matthew D'Ancona, Sunday Telegraph
'A lucid explanation of how to think probabilistically' Guardian
PUBLISHERS WEEKLY
Despite the fact that there is more information about everything from finance to professional sports available than ever before, predictions "may be more prone to failure" in this "era of Big Data." Balancing technical detail and thoughtful analysis with fluid prose, statistician Silver (FiveThirtyEight ) picks apart the many ways in which predictions in various fields have been flawed, while suggesting approaches that could improve the practice. The catastrophic miscalculations on the part of financial lending agencies that led to the recession of 2008 arose for the same types of reasons that caused baseball scouts to undervalue Boston Red Sox all-star player Dustin Pedroia or feed into a political pundit's flawed forecast: overconfidence in models based on oversimplified principles and unrealistic initial assumptions. Though there is no simple solution, a Bayesian methodology, in which prior beliefs are taken into account and initial assumptions constantly revised, would lead to more accurate predictive models. Effective prediction requires, according to Silver, "the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference."
