First, we need to distinguish Silver’s state-by-state prediction with his “win probability”. The former was pretty unremarkable in 2016, and I think we can agree that like everyone else he incorrectly predicted WI, MI, and PA.
However, his win probability is a different algorithm. It considers alternate scenarios, eg Trump wins Pennsylvania but loses Michigan. It somehow finds the probability of each scenario, and somehow calculates a total probability of winning. This does not correspond to one specific set of states that Silver thinks Trump will win. In 2016, it came up with a 28% probability of Trump winning.
You say that’s not “getting it wrong”. In that case, what would count as “getting it wrong”? Are we just supposed to have blind faith that Silver’s probability calculation, and all its underlying assumptions, are correct? Because when the candidate with a higher win probability wins, that validates Silver’s model. And when that candidate loses, that “is not evidence of an issue with the model”. Heads I win, tails don’t count.
If I built a model with different assumptions and came up with a 72% probability of Trump winning in 2016, that differs from Silver’s result. Does that mean that I “got it wrong”? If neither of us got it wrong, what does it mean that Trump’s probability of winning is simultaneously 28% and 72%?
And if there is no way for us to tell, even in retrospect, whether 28% is wrong or 72% is wrong or both are wrong, if both are equally compatible with the reality of Trump winning, then why pay any attention to those numbers at all?
First, we need to distinguish Silver’s state-by-state prediction with his “win probability”. The former was pretty unremarkable in 2016, and I think we can agree that like everyone else he incorrectly predicted WI, MI, and PA.
However, his win probability is a different algorithm. It considers alternate scenarios, eg Trump wins Pennsylvania but loses Michigan. It somehow finds the probability of each scenario, and somehow calculates a total probability of winning. This does not correspond to one specific set of states that Silver thinks Trump will win. In 2016, it came up with a 28% probability of Trump winning.
You say that’s not “getting it wrong”. In that case, what would count as “getting it wrong”? Are we just supposed to have blind faith that Silver’s probability calculation, and all its underlying assumptions, are correct? Because when the candidate with a higher win probability wins, that validates Silver’s model. And when that candidate loses, that “is not evidence of an issue with the model”. Heads I win, tails don’t count.
If I built a model with different assumptions and came up with a 72% probability of Trump winning in 2016, that differs from Silver’s result. Does that mean that I “got it wrong”? If neither of us got it wrong, what does it mean that Trump’s probability of winning is simultaneously 28% and 72%?
And if there is no way for us to tell, even in retrospect, whether 28% is wrong or 72% is wrong or both are wrong, if both are equally compatible with the reality of Trump winning, then why pay any attention to those numbers at all?