How can you falsify the claim “Clinton has a higher chance of winning”?
Alternately:
Silver said “Clinton has a higher chance of winning in 2016” whereas Michael Moore said “Trump has a higher chance of winning in 2016”.
In hindsight, is one of these claims more valid than the other? Because if two contradictory claims are equally valid, then they are both meaningless.
You can’t really falsify the claim “Clinton has a higher chance of winning”, at least the way Nate Silver models it. His model is based upon statistics, and he basically runs a bunch of simulations of the election. In more of these simulations, Clinton won, hence his claim. But we had exactly one actual election, and in the election, Trump won. Perhaps his model is just wrong, or perhaps the outcome matched one of the simulations in his model where Trump won. If we could somehow run the election hundreds of times (or observe what happened in hundreds of parallel universes) then maybe we could see if his model matched the outcome of a statistically significant number of election results. But nevertheless, Nate Silver had a model and statistics to back up his claim.
As for Michael Moore, I’m not sure exactly how he came up with his prediction, but I get the impression it was mostly a gut feeling based upon his observations of what was happening. Nevertheless, Michael Moore still could back up his statement by articulating why he was claiming that and the observations he had made.
Though one crucial difference is still the whole prediction thing. Michael Moore actually made a prediction of a Trump win. Whereas Nate Silver just stated that Clinton had a higher chance of winning, and once again that was not a prediction. So you’re really comparing two different things here.
Silver claimed that Trump had a 28% chance of winning in 2016.
Suppose I built a model that claimed Trump had a 72% chance of winning in 2016.
Given there is only one 2016 election and Trump won it, is there any reason to believe that Silver’s results are better or worse than mine?
Sure, you could present your model and the data it is based upon and everyone could make their judgement.