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Nate Silver

Nate Silver Quotes

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Famous Nate Silver Quotes

“In any contentious debate, some people will find it advantageous to align themselves with the crowd, while a smaller number will come to see themselves as persecuted outsiders. This may especially hold in a field like climate science, where the data is noisy and the predictions are hard to experience in a visceral way. And it may be especially common in the United States, which is admirably independent-minded.”

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Book:The Signal and the Noise: Why So Many Predictions Fail—But Some Don't
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“Most of you will have heard the maxim "correlation does not imply causation." Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don't light a patch of the Montana brush on fire when you buy a pint of Haagan-Dazs.”

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Book:The Signal and the Noise: Why So Many Predictions Fail—But Some Don't
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“Indeed, the big brokerage firms tend to avoid standing out from the crowd, downgrading a stock only after its problems have become obvious. In October 2001, fifteen of the seventeen analysts following Enron still had a “buy” or “strong buy” recommendation on the stock even though it had already lost 50 percent of its value in the midst of the company’s accounting scandal.”

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Book:The Signal and the Noise: Why So Many Predictions Fail—But Some Don't
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“The Bayesian Invisible Hand … free-market capitalism and Bayes’ theorem come out of something of the same intellectual tradition. Adam Smith and Thomas Bayes were contemporaries, and both were educated in Scotland and were heavily influenced by the philosopher David Hume. Smith’s 'Invisible hand' might be thought of as a Bayesian process, in which prices are gradually updated in response to changes in supply and demand, eventually reaching some equilibrium. Or, Bayesian reasoning might be thought of as an 'invisible hand' wherein we gradually update and improve our beliefs as we debate our ideas, sometimes placing bets on them when we can’t agree. Both are consensus-seeking processes that take advantage of the wisdom of crowds. It might follow, then, that markets are an especially good way to make predictions. That’s really what the stock market is: a series of predictions about the future earnings and dividends of a company. My view is that this notion is 'mostly' right 'most' of the time. I advocate the use of betting markets for forecasting economic variables like GDP, for instance. One might expect these markets to improve predictions for the simple reason that they force us to put our money where our mouth is, and create an incentive for our forecasts to be accurate. Another viewpoint, the efficient-market hypothesis, makes this point much more forcefully: it holds that it is 'impossible' under certain conditions to outpredict markets. This view, which was the orthodoxy in economics departments for several decades, has become unpopular given the recent bubbles and busts in the market, some of which seemed predictable after the fact. But, the theory is more robust than you might think. And yet, a central premise of this book is that we must accept the fallibility of our judgment if we want to come to more accurate predictions. To the extent that markets are reflections of our collective judgment, they are fallible too. In fact, a market that makes perfect predictions is a logical impossibility.”

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Book:The Signal and the Noise: Why So Many Predictions Fail—But Some Don't
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“The most robust evidence indicates that this wisdom-of-crowds principle holds when forecasts are made independently before being averaged together. In a true betting market (including the stock market), people can and do react to one another’s behavior.”

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Book:The Signal and the Noise: Why So Many Predictions Fail—But Some Don't
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“As the statistician George E. P. Box wrote, "All models are wrong, but some models are useful." What he meant by that is that all models are simplifications of the universe, as they must necessarily be. As another mathematician said, "The best model of a cat is a cat." ... The key is in remembering that a model is a tool to help us understand the complexities of the universe, and never a substitute for the universe itself.”

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Book:The Signal and the Noise: Why So Many Predictions Fail—But Some Don't
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“Expert estimates of probability are often off by factors of hundreds or thousands. [...] I used to be annoyed when the margin of error was high in a forecasting model that I might put together. Now I view it as perhaps the single most important piece of information that a forecaster provides. When we publish a forecast on FiveThirtyEight, I go to great lengths to document the uncertainty attached to it, even if the uncertainty is sufficiently large that the forecast won't make for punchy headlines.”

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Author:Nate Silver
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