<commit_before># All Models Are Wrong, Some are Useful

In 1976, a British statistician named <NAME> wrote the famous line, “All models are wrong, some are useful.” 

His point was that we should focus more on whether something can be applied to everyday life in a useful manner rather than debating endlessly if an answer is correct in all cases. As historian <NAME> puts it, “Scientists generally agree that no theory is 100 percent correct. Thus, the real test of knowledge is not truth, but utility. Science gives us power. The more useful that power, the better the science.”

# Prediction

Wall Street always overflows with Holy Grails—those predictions, strategies, secret formulas, and genius interpretations that promise otherworldly knowledge and riches if you just trust. They are most often delivered in the investment world through a black box—a closed system where the inputs and outputs are known, but the internal analytical workings are left top secret, only for the high priests’ consumption. Black box positioning goes far beyond markets, however. It is not surprising in a modern, interconnected age that when you take a very smart guy, rows of computers, proprietary formulas, and code that only the one smart guy can see, and then add a string of successful forecasts, boom—you end up with a nerdy, made-for-social-media superstar who suddenly makes prediction cool for the proletariat.

<NAME> is that guy. Consider:

* He successfully called the outcomes in 49 of the 50 states in the 2008 U.S. presidential election.
* He successfully called the outcomes in 50 of the 50 states in the 2012 U.S. presidential election.

We’ll never really know whether a particular forecast was correct or incorrect, since they’re all probabilistic, and they all suggest a Clinton win is the most likely outcome.  Yet for anyone who followed the 2016 presidential race, Silver’s political predictions hit the wall.

On Trump’s win, Silver said as much, “It’s the most shocking political development of my lifetime.” But no one can predict outliers, so if someone like Silver pretends he can—watch out. <NAME>, in his famed 1979 paper “Accuracy of Forecasting: An Empirical Investigation,” showed that simple beats complicated and that moving averages beat tortuous econometric routines. 

<NAME> had a great piece in Psychology Today nailing the conundrum:

| “Some parts of accounting and consulting make their money by helping executives experience a perception of increasing certainty, through strategic planning and ‘forecasting’. While the financial markets of 2008 showed once again that the future is inherently uncertain, the one thing that’s certain is that people will pay lots of money to at least feel less uncertain. That’s because uncertainty feels, to the brain, like a threat to your life.”
<commit_msg>Update all-models-are-wrong.md<commit_after># All Models Are Wrong, Some are Useful

In 1976, a British statistician named <NAME> wrote the famous line, “All models are wrong, some are useful.” 

His point was that we should focus more on whether something can be applied to everyday life in a useful manner rather than debating endlessly if an answer is correct in all cases. As historian <NAME> puts it, “Scientists generally agree that no theory is 100 percent correct. Thus, the real test of knowledge is not truth, but utility. Science gives us power. The more useful that power, the better the science.”

# Prediction

Wall Street always overflows with Holy Grails—those predictions, strategies, secret formulas, and genius interpretations that promise otherworldly knowledge and riches if you just trust. They are most often delivered in the investment world through a black box—a closed system where the inputs and outputs are known, but the internal analytical workings are left top secret, only for the high priests’ consumption. Black box positioning goes far beyond markets, however. It is not surprising in a modern, interconnected age that when you take a very smart guy, rows of computers, proprietary formulas, and code that only the one smart guy can see, and then add a string of successful forecasts, boom—you end up with a nerdy, made-for-social-media superstar who suddenly makes prediction cool for the proletariat.

<NAME> is that guy. Consider:

* He successfully called the outcomes in 49 of the 50 states in the 2008 U.S. presidential election.
* He successfully called the outcomes in 50 of the 50 states in the 2012 U.S. presidential election.

We’ll never really know whether a particular forecast was correct or incorrect, since they’re all probabilistic, and they all suggest a Clinton win is the most likely outcome.  Yet for anyone who followed the 2016 presidential race, Silver’s political predictions hit the wall.

On Trump’s win, Silver said as much, “It’s the most shocking political development of my lifetime.” But no one can predict outliers, so if someone like Silver pretends he can—watch out. <NAME>, in his famed 1979 paper “Accuracy of Forecasting: An Empirical Investigation,” showed that simple beats complicated and that moving averages beat tortuous econometric routines. 

<NAME> had a great piece in Psychology Today nailing the conundrum:

> “Some parts of accounting and consulting make their money by helping executives experience a perception of increasing certainty, through strategic planning and ‘forecasting’. While the financial markets of 2008 showed once again that the future is inherently uncertain, the one thing that’s certain is that people will pay lots of money to at least feel less uncertain. That’s because uncertainty feels, to the brain, like a threat to your life.”

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