The problem with statistical models and forecasting
A portion of the cover for the book, "The Signal and The Noise: Why Most Predictions Fail — But Some Don't" by Nate Silver.
If you really what to know what's going on in any even-remotely quantifiable discipline, nothing paints a clearer picture than the numbers, right? They end arguments. They can help forecast what's going to happen. They are, simply put, the facts. Not always.
In his new book, "The Signal and the Noise," author Nate Silver argues that numbers are part of the challenge of painting a clear picture of the American economy. The first example that Silver uses to illustrate his point is the housing crisis.
"You had these credit rating agencies who were taking these very novel types of debt instruments, mortgage-backed securities, and giving them very high ratings -- implying that they had almost no chance of defaulting when there really wasn't much reason to believe that if you actually sorted through the details of these," he says.
Writing this book, Silver says he learned that people have too much trust in statistical models and not enough cognizance to realize just how noisy the data can be. In his book, he stresses the importance of communicating uncertainty. He says that numbers can be misread and that the American economy is constantly changing -- making it difficult to forecast.
"It is very difficult, for example, to measure the effects of different types of stimulus. The reason is that you're going back and saying, 'Well when the economy was bad before, what happened to it?' But there were different economic policies attempted in every one of those recessions," he says. "So it's never a total experiment where you're just sitting there and observing things. You're trying to intervene in the economy and make it better."
Read more from Silver at his political forecasting blog FiveThirtyEight, which currently appears on the website of the New York Times. And listen to our full interview with him by clicking play on the audio player above.