Nate Silver writes on how he got Donald Trump so wrong (as almost everyone did). He notes:
But it’s not how it worked for those skeptical forecasts about Trump’s chance of becoming the Republican nominee. Despite the lack of a model, we put his chances in percentage terms on a number of occasions. In order of appearance — I may be missing a couple of instances — we put them at 2 percent (in August), 5 percent (in September), 6 percent (in November), around 7 percent (in early December), and 12 percent to 13 percent (in early January).
Silver notes five things:
- Our early forecasts of Trump’s nomination chances weren’t based on a statistical model, which may have been most of the problem.
- Trump’s nomination is just one event, and that makes it hard to judge the accuracy of a probabilistic forecast.
- The historical evidence clearly suggested that Trump was an underdog, but the sample size probably wasn’t large enough to assign him quite so low a probability of winning.
- Trump’s nomination is potentially a point in favor of “polls-only” as opposed to “fundamentals” models.
- There’s a danger in hindsight bias, and in overcorrecting after an unexpected event such as Trump’s nomination.
The interesting thing is that if you just looked at the polls, then you should have concluded Trump would win. He basically led in every poll for six months. But everyone found reasons to argue why the polls would change. 538 for example places great store on endorsements. And endorsements have been a good predictor in previous elections, but as Silver notes the sample size of previous elections is not great.
So one lesson from this is not to ignore the polls. They’re not always right, but polls vs assumptions, polls tend to win out.
Another source of info I look to is the prediction markets. At the moment they have Clinton at 66% likely to win and Trump 32%.