On Oct. 30, the Chicago Cubs were down three games to one, just a loss away from blowing their chance at a first World Series championship in more than a century. Analysts said the Cubs’ chances were only 13 to 15 percent. “The Cubs have a smaller chance of winning than Trump does,” announced FiveThirtyEight, which put Trump’s chances at 21 percent.If only journalists and pundits had taken and absorbed a classic in statistics.
The Cubs then staged a dramatic comeback and won the World Series in what reporters called one of the greatest games ever, if not the greatest ever. And although Cleveland Indians fans were understandably heartbroken, they did not rush to condemn the data analysts. Reporters and pundits did not spill gallons of ink bemoaning the fact that “data died tonight” or that “the data broke.”
Yet this was exactly the reaction to Donald Trump’s presidential victory — even though it was arguably just as likely and maybe even more likely than the Cubs’ victory. For weeks, we heard a dramatic narrative of polls gone wildly astray and a “devastating blow to the credibility” of the supposed experts.
One thing is clear: People don’t understand the uncertainty underlying predictions — or at least they better understand uncertainty in sports than in elections. Perhaps this is because there are more baseball games than elections. Or perhaps sports writers have an incentive to push the “ain’t over till it’s over” narrative while political reporters face more complicated incentives, pressured both by the public’s desire for certainty and the newsroom’s interest in a close horse race.
Regardless of the explanation, a widespread resistance to thinking probabilistically about the election was the bigger problem than polling errors. Here’s one way to help people understand probabilities better.
Wednesday, November 30, 2016
The Washington Post reports:
Posted by Steve Bartin at 6:26 PM