Back when I used to teach at Berkeley, I used to run into non-Bayesian hardliners–the kind of people who would say no to prior information even if it were wrapped nicely, placed on a warm plate, and served with a delicious pile of crisp fries. I don’t run into such people much anymore but then recently Matthew Yglesias linked to my mention of economy-based election forecasts. I read the comments, one of which says:

The problem with this sort of analysis is that we’re working with very few data points. We’ve only ever had 50 some odd Presidential elections. 12 in the television era. 2 in the internet era. It’s very very hard to generalize anything from a small dataset, and even correlations don’t mean much – who’s to say that the macroeconomic correlations are any more meaningful than the Redskins game?

I agree with the bit about the small sample size–as I often tell people–95% intervals on the national election outcome don’t mean much considering we wouldn’t even try to apply a single model to 20 successive elections–but . . . “who’s to say that the macroeconomic correlations are any more meaningful than the Redskins game?” ???

The answer is: Everyone can agree that macroeconomic correlations are more meaningful than the Redskins game. For one thing, voters when surveyed say that the economy is important to their voting. They don’t say that the Redskins game is important. Also most people don’t care about the Redskins. Etc etc. In statistics we call this prior information. Anyway, I’m not trying to pick on a blog commenter–not everyone’s an expert in every field of endeavor–it was just funny to see such a pure non-Bayesian in the wild, so to speak.