In 2305 today we looked at Nate Silver's bad call during the primary. Along with most other political observers, he discounted Trump's viability as a candidate for the Republican nomination. Here he tries to figure out what he got wrong and why. He calls himself a "data journalist" which suggests that his approach is rigorous and needs to be adjusted if proven faulty. Here's what he came up with.
- Click here for the article.
- Click here for the article.
. . . I’m going to proceed in five sections:
1. Our early forecasts of Trump’s nomination chances weren’t based on a statistical model, which may have been most of the problem.
2. Trump’s nomination is just one event, and that makes it hard to judge the accuracy of a probabilistic forecast.
3. 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.
4. Trump’s nomination is potentially a point in favor of “polls-only” as opposed to “fundamentals” models.
5. There’s a danger in hindsight bias, and in over correcting after an unexpected event such as Trump’s nomination.
