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I think journalists have missed the point about Nate Silver’s error. Since Silver personifies data analysis, it is easy to get mixed up about what failed. As I wrote last week, the data didn’t fail – clear signs pointed toward Trump for a long time. However, Silver went beyond the data – in his words, he “acted like a pundit.” Here are his comments. The essay is long, but the title is on point. Basically I agree with points #1 (he didn’t make a real statistical model) and #4 (“fundamentals”-based models might not add that much value).
A reader asks what I think of the claim in point #3 that he was “too frequentist” and that his “Bayesian prior” of a Trump nomination should have been 10-12%. Hmmm. My first thought is that estimation of priors requires a lot of judgment. I don’t fault him for that…but he should own his estimates. To my taste, he leans too hard on political science, which relies on nice, stable trends. In a disruptive race like the 2016 GOP nomination contest, this leads to problems.
http://election.princeton.edu/
A reader asks what I think of the claim in point #3 that he was “too frequentist” and that his “Bayesian prior” of a Trump nomination should have been 10-12%. Hmmm. My first thought is that estimation of priors requires a lot of judgment. I don’t fault him for that…but he should own his estimates. To my taste, he leans too hard on political science, which relies on nice, stable trends. In a disruptive race like the 2016 GOP nomination contest, this leads to problems.
http://election.princeton.edu/
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