Nate Silver ("The Signal and the Noise") on Statistics and Forecasting
Nate Silver's The Signal and the Noise (our Spotlight pick for September's Best Books of the Month) is "a timely and readable reminder that statistics are only as good as the people who wield them," as our reviewer Darryl Campbell put it. In this exclusive Q&A, Nate explains the success of weather forecasters and gamblers. Scroll down and watch a video of Silver discussing the book.
Why do you think statistics books continue to capture the popular imagination, from Freakonomics to Moneyball?
We encounter so much information today that people are naturally curious about what in the heck we should do with all of it. And we’re becoming less trusting of institutions that mediate information, like the news media. We have all this data, and we want to learn for ourselves what it all means.
A little bit of math and statistics and probability and logic helps us with our information-processing goals. But what’s great about books like Moneyball and Freakonomics is that they make statistics approachable. Subjects like English and history are taught in very hands-on ways--you read great books, discuss the ideas and characters, and it’s easy to understand their relevance. Whereas math is taught in very abstract and technical ways--even though it’s just as relevant to our everyday lives, and just as intuitive if it’s taught well.
Books like Freakonomics and Moneyball help to bridge that gap. They’re sort of making up for the calculus teacher that had you memorize one too many derivatives and turned you off to the subject as a result. Not that there’s anything wrong with calculus.
Politics and baseball, the two subjects you are best known for, are just part of the book. Why was it important to include so many different fields--economics, earth and life sciences, games, even terrorism?
One thing that baseball fans know is to be wary of small sample sizes. If you show up at the ballpark, and the catcher gets three hits that day, that doesn’t really tell you very much about how good he really is. It takes a long time--hundreds of at-bats--for the signal to emerge since there’s so much luck in the game.
But in the same way, I thought, perhaps baseball is an exceptional case. Are there Moneyball-like success stories in other fields in which statistics and analysis and prediction is pertinent?
In fact, I found that there are entire disciplines in which our analysis has failed to produce much progress, at least as measured by our ability to make reliable predictions. Finance and economics are obvious examples of this, for instance. Economists have literally tens of thousands of data series to mine--more statistics than baseball geeks do. But they still aren’t able to predict recessions more than a few months in advance.
The book needed to cover a diverse enough range of examples that I could to some systematic conclusions about why predictions succeed and why they fail in different fields. And that meant going beyond the cases that were most familiar to me when I started to write and research the book.
Often, the conclusions were surprising. I’d thought that weather forecasting was a hopeless case, for instance, but it turned out to be a huge success story. Meteorologists and professional gamblers basically emerge as the heroes of the book.
Rather controversially you say in the book “we can never make perfectly objective predictions. They will always be tainted by our subjective point of view.” How so?
Well, I think human beings are pretty darned smart. Our brains can store about three terabytes of information, which is just an enormous amount. And a three-year old is able to do a lot of things that a supercomputer can’t.
Still, three terabytes represents only about one one-millionth of the information that IBM says is now being produced in the world each day. We have to be terribly selective about the information that we choose to remember. That necessarily implies that we have a point of view--the set of facts that I have at my command won’t be the same as yours. And we have to make approximations, whether it’s in the form of our language, or the mathematical models that we design.
Even the things we take most for granted, like our sensory inputs (vision, hearing, etc.) rely heavily on making approximations about the objective world. We’re just taking in way more information out there than our brains can process.
So it’s absolutely delusional to think that any one of us has a monopoly on the truth--that our beliefs about the world aren’t flawed in any number of large and small ways. The book, in some ways, is about accepting our flaws, as well as recognizing the things that we’re good at.
What distinguishes those who are good at forecasting to those who seem to get it all wrong?
The whole book is an answer to that question. But here’s one big thing that weather forecasters and gamblers--two of our success stories--have in common. They recognize that their knowledge of the world is imperfect. They express their predictions in terms of probabilities: there’s a 40-percent chance of rain tomorrow; there’s a 30-percent chance that I’ll catch a card to make a flush and win a huge poker hand.
This type of thinking turns out to be extremely important when it comes to sorting through the enormous amount of information that we encounter today.
>Read the full Q&A here.