Take polling data then randomly roll for some error within the margin of error for each state. Then adjust based on bias. Do this n times and get your prediction.
They didn't get a certain answer so it was useless this time, but if it had turned out that somehow one candidate won a supermajority of simulations then you got useful information. Can't know unless you ask.
Yes, it's possible that despite close in polls, the margin of error would favour one candidate being more likely to win.
"Everyone knows" something until there's evidence to the contrary.
The polls and outcome could still be wrong if something unexpected happens, like if all the people who don't usually vote decide this is the most important election and vote for the first and only time in their life.
![[Not the Onion] Simulations](https://hexbear.net/pictrs/image/eb31da58-12e6-442a-802a-bb751b72cecf.png){kind=link}
Take polling data then randomly roll for some error within the margin of error for each state. Then adjust based on bias. Do this n times and get your prediction.
Is there a point to this? From the tweet it doesn't give new information. It just reaffirms that three contest will be close which everyone knew.
They didn't get a certain answer so it was useless this time, but if it had turned out that somehow one candidate won a supermajority of simulations then you got useful information. Can't know unless you ask.
There is utility in some cases; there's correlations between polling and areas, and the electoral college makes it all complicated (by design)
But, like, here? No, no practical difference between any of the 'models' all showing some variation of 'its a toss up'
Yes, it's possible that despite close in polls, the margin of error would favour one candidate being more likely to win.
"Everyone knows" something until there's evidence to the contrary.
The polls and outcome could still be wrong if something unexpected happens, like if all the people who don't usually vote decide this is the most important election and vote for the first and only time in their life.