Humans have plenty of cognitive strengths, but one area that most of us struggle with is estimating, explaining and preparing for improbable events. This theme underpins two of Nassim Taleb’s major works: Fooled by Randomness and The Black Swan: The Impact of the Highly Improbable. In the latter work, Taleb defines a black swan events as having three characteristics: the event is a surprise (to the observer), it has a major effect, and _people incorrectly try to rationalize it in hindsight _(emphasis mine).

Taleb focuses on black swan events on the world stage, such as the creation of the internet, World War I, and the dissolution of the Soviet Union. I believe that humans behave similarly, and to their detriment, with less unlikely events predicted by statistical models as long as they’re important. We’ve seen this recently in the results of the 2016 presidential election results. Models published by The Huffington Post, The New York Times, and 538 all had Hillary Clinton at a 71–98% chance of winning. After she lost the election, a common reaction was whether we could trust polls [and the models that rely on their data] again, which I was surprise to see even from statistically minded friends. Why is our gut reaction to scrutinize the model that said Trump had a 29% chance of winning? We wouldn’t question the fairness of a coin that comes up tails twice (25% chance) or a 1 of a rolled die (17% chance); both of which are lower than the odds given to Trump winning the 2016 election.

I posit that this reaction, which is a form of rationalization by hindsight, is due to the lack of a mental framework to grapple with real world event probabilities, and that not having this framework can result in irrational decision-making. In order to illustrate this, let’s move away from the world of election modeling to a more common situation encountered in the business world.

#analytics #statistics #data-science #probability #decision-making #data analytic