1) His model is easy to verify... wait for the elections to be over, and see how much his predictions correlate with reality. He predicts a great many races, and the results speak for themselves.
"The accuracy of his November 2008 presidential election predictions—he correctly predicted the winner of 49 of the 50 states—won Silver further attention and commendation. The only state he missed was Indiana, which went for Barack Obama by 1%. He also correctly predicted the winner of all 35 Senate races that year."
2) Also, his model accounts for the response rate figures you cite - that was actually the point of his most recent blog post. The reason Nate gives Romney any chance at all to win is because his model predicts that there is less than 1/5 chance that the polls are systematically biased (based on data since 1968). He thinks that if he's wrong, it's exactly for the reason you say - that those who respond to the polls are a small group that don't represent the population. He has a large enough sample size to eliminate sampling error, and we're close enough to election day to discount error due to polls being a snapshot in time. So there is only systematic bias left to discuss...
"So why, then, do we have Mr. Obama as “only” an 83.7 percent favorite to win the Electoral College, and not close to 100 percent? This is because of the other potential sources of error in polling."
And that error is simply that polls don't reflect reality, and he thinks that is about a 15% chance.
He's not the Oracle at Delphi; he gives probability estimates, not inescapable prophecy. It's inevitable that some elections will go to the candidate who he predicts has a lower chance of winning. The real question is, how often? If it happens more or less often than he predicts, then his predictions are biased and should update on that fact.
One factor that messes with any model is lack of consistent data. House races aren't publicly polled nearly as often as statewide polls. And furthermore, it's not necessarily that he "got races wrong." When he says Obama wins 80% of the time, he's also saying that Romney wins 20% of the time. 1 out of 5 times, Romney will win and Obama will lose, despite being a favorite to win.
It is important to note that the cited accuracy was for the predictions made the day before (or perhaps even later) the election. It would be interesting to know how accurate the predictions were X weeks out.
1) His model is easy to verify... wait for the elections to be over, and see how much his predictions correlate with reality. He predicts a great many races, and the results speak for themselves.
"The accuracy of his November 2008 presidential election predictions—he correctly predicted the winner of 49 of the 50 states—won Silver further attention and commendation. The only state he missed was Indiana, which went for Barack Obama by 1%. He also correctly predicted the winner of all 35 Senate races that year."
http://en.wikipedia.org/wiki/Nate_Silver
2) Also, his model accounts for the response rate figures you cite - that was actually the point of his most recent blog post. The reason Nate gives Romney any chance at all to win is because his model predicts that there is less than 1/5 chance that the polls are systematically biased (based on data since 1968). He thinks that if he's wrong, it's exactly for the reason you say - that those who respond to the polls are a small group that don't represent the population. He has a large enough sample size to eliminate sampling error, and we're close enough to election day to discount error due to polls being a snapshot in time. So there is only systematic bias left to discuss...
"So why, then, do we have Mr. Obama as “only” an 83.7 percent favorite to win the Electoral College, and not close to 100 percent? This is because of the other potential sources of error in polling."
And that error is simply that polls don't reflect reality, and he thinks that is about a 15% chance.
http://fivethirtyeight.blogs.nytimes.com/2012/11/03/nov-2-fo...