[Tinordi]

Stats coldly eliminate wishful thinking. Which is why stats are universally a better heuristic for prediction that human bias which is too context dependent for predictive use.

Daniel Kahneman:

Orley Ashenfelter has offered a compelling demonstration of the power of simple statistics to outdo world-renowned experts. Ashenfelter wanted to predict the future value of fine Bordeaux wines from information available in the year they are made.

Ashenfelter converted that conventional knowledge into a statistical formula that predicts the price of a wine - for a particular property and at a particular age - by three features of the weather: <<Age of prospect>> the average temperature over the summer growing season, <<Point production of prospect>> the amount of rain at harvest-time, <<point production of linemates>> and the total rainfall during the previous winter. His formula provides accurate price forecasts years and even decades into the future. Indeed, his formula forecasts future prices much more accurately than the current prices of young wines do.

Ashenfelter’s formula is extremely accurate - the correlation between his predictions and actual prices is above .90.

Why are experts inferior to algorithms? One reason, which Meehl suspected, is that experts try to be clever, think outside the box, and consider complex combinations of features in making their predictions. Complexity may work in the odd case, but more often than not it reduces validity. Simple combinations of features are better.

Human decision makers are inferior to a prediction formula even when they are given the score suggested by the formula! They feel that they can overrule the formula because they have additional information.

There are few circumstances under which it is a good idea to substitute judgment for a formula. In a famous thought experiment, he described a formula that predicts whether a particular person will go to the movies tonight and noted that it is proper to disregard the formula if information is received that the individual broke a leg today. The name “broken-leg rule” has stuck. The point, of course, is that broken legs are very rare - as well as decisive.

To maximize predictive accuracy, final decisions should be left to formulas, especially in low-validity environments.

http://sivers.org/book/ThinkingFastAndSlow