[martingoodson]

If one candidate won 1000 consecutive polls would it also tell you nothing about your estimate? This is obviously absurd: of course it would.

How about 100 times? 10 times? 2 times?

At what point does evidence cease to 'say anything about the quality of our analysis'? The answer is never. Every datapoint can be used to update your priors according to bayesian statistics.

[WoahNoun]

As I said in my other comment that you chose to ignore, the probability of winning national popular vote does not indicate the probability of who will be President.

[martingoodson]

It's not that I chose to ignore you. I just didn't find your comment interesting from the statistical perspective.

[WoahNoun]

You don't find aggregation of regional deviation to national deviation interesting from a statistical perspective? Odd.

Especially for someone who titles them-self "Chief Scientist"

[martingoodson]

This is hacker news: I'm allowed to be more interested in bayesian statistics than your country's electoral system.

[WoahNoun]

>This is hacker news: I'm allowed to be more interested in bayesian statistics than your country's electoral system.

Aren't regional probabilities just conditions on the national distribution? Building the national Presidential election distribution from the conditioned distributions versus just using the sampled national distribution is bayesian statistics.

>The probability of winning is based on the popular support for a candidate

This was the argument you made. And I don't see how this is Bayesian vs frequency when all I am saying is that the same national popular vote distribution can have large variance in regional conditional distributions which leads to large variance in election outcome distribution due to the electoral college.

Sounds pretty bayesian to me.