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The Signal and the Noise

Why So Many Predictions Fail — but Some Don't

By Nate Silver
19-minute read
Audio available
The Signal and the Noise: Why So Many Predictions Fail — but Some Don't by Nate Silver

The Signal and the Noise explains why so many expert predictions today fail spectacularly, and what statistical and probability tools are more up to the task of predicting real-world phenomena.

  • Anyone whose job involves making predictions or forecasts
  • Anyone who wants to know why the economy is so difficult to predict

Nate Silver is a statistician and writer who specializes in analyzing baseball and elections. He is perhaps most famous for correctly predicting the result of the 2008 US presidential election for 49 out of 50 states.

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The Signal and the Noise

Why So Many Predictions Fail — but Some Don't

By Nate Silver
  • Read in 19 minutes
  • Audio & text available
  • Contains 12 key ideas
Upgrade to Premium Read or listen now
The Signal and the Noise: Why So Many Predictions Fail — but Some Don't by Nate Silver
Synopsis

The Signal and the Noise explains why so many expert predictions today fail spectacularly, and what statistical and probability tools are more up to the task of predicting real-world phenomena.

Key idea 1 of 12

Economists are bad at both forecasting the economy and estimating the certainty of their forecasts.

Will you walk to work today or take the bus? Will you take an umbrella or not?

In our everyday lives, we make decisions based on predictions of what will happen in the future, like whether it will rain or shine.

But predictions are also common in the public realm: stock market analysts, meteorologists and sports commentators all make a living out of them.

One area where one might expect particularly good predictions is the economy. After all, it’s of crucial importance for individuals, companies and even nations, and there’s a wealth of data available: some companies track as many as four million economic indicators.

But despite these factors, economists have an atrocious track record in forecasting.

Consider the commonly predicted economic indicator, the gross domestic product (GDP).

The first problem with GDP predictions is that economists often make predictions like “Next year, GDP will increase by 2.7 percent.” In fact, they’ve derived this figure from a broad prediction interval that says something like “It is 90 percent likely that GDP growth will fall somewhere between 1.3 and 4.2 percent.” So an exact number as a prediction is misleading, as it gives a false sense of precision and security.

What’s worse, economists aren’t actually very good at coming up with prediction intervals either. If their 90 percent prediction intervals were roughly accurate, one would expect the actual GDP to only fall outside the prediction interval one out of ten times. However, a poll of professional forecasters shows that, since 1968, they’ve been wrong roughly half the time. Therefore, it seems that economists’ are not only poor predictors but also gravely overestimate their predictions’ certainty.

Besides GDP predictions, economists are also spectacularly bad at forecasting depressions. Consider that, in the 1990s, economists were only able to predict two out of the sixty depressions that had occurred worldwide one year ahead of time.

To put it kindly, economic predictions should be taken with at least a grain of salt.

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