[snickerbockers]

I don't see the point in anything that isn't falsifiable, and in order for that to happen there needs to be a sample size greater than one. If Hillary has a 99.9% chance of winning and Donald has a 0.1% chance of winning, does that mean the model was wrong or does that mean we're in the 0.1% timeline?

If you want to prove that a coin flip has a 50% chance of landing heads, a 50% chance of landing on tails, and a negligible chance of landing on its edge, you can run as many tests as you want and observe that as N approaches infinity the number of heads converges on 0.5N and the number of tails converges on 0.5N. Alternatively, you might find that the coin isn't well-balanced, in which case you've proven that the "50/50 model" was not accurate.

You can't do that with elections because each election only happens once. Even if the same two candidates are running against each other in every election, the issues at stake are different and the voterbase is different. In reality one candidate has a 100% chance of victory and the other has a 0% chance of victory but we don't know which candidate it is.