[yk]

Somewhat interesting, however the guy lost me more and more the longer he argues. So, the various anomalies in the dataset are somewhat interesting, but having weird outliers in the margins is an entirely expected effect. Just because there are not many datapoints. So when you filter for something marginal like Trump winning New Jersey, then the statistical error increases and therefore it is entirely unsurprising that something weird happens. Thankfully, these systems are designed to work with probabilities, and these outliers are weighted down.

Additionally, getting worked up about a 3% chance of Biden winning Alabama. I mean, what does a 3% chance even mean for a one off event, compared to a 5% chance or a .3% chance? I know fully well, that it means I should bet $100 if I can get more than $3000 payout, but the trouble is that is only if we bet often enough. (Perhaps often enough on different things.) For a one off thing, the important part is, it is with a very high degree of certainty a loss of $100. So any claims that Bidens chances of winning are too high should be regarded with high suspicion.

Also, I listened eralier to Nate Silver's model talk [0], where he discusses quite a few problems with low quality polls in some states.

[0] https://fivethirtyeight.com/features/politics-podcast-nation...

[RivieraKid]

> Just because there are not many datapoints.

There are more than enough data points to determine the between-state error correlations, many of which seem to be very off.

> Additionally, getting worked up about a 3% chance

The weird between-state correlations actually have a large effect, they increase state and nationwide uncertainty and as a result Trump has a higher chance of winning.

[noirbot]

It seems more likely that it's actually the other way. Nate Silver has specifically said he built the model to have relatively high uncertainty, especially with the volatility of this year, so this seems more like the outcome of intentional decisions to not let the model be overly confident.

[RivieraKid]

I think it's an error in the model structure. If their goal was to artificially increase uncertainty, there are more reasonable ways to do that than adding weird between-state correlations (like the uncertainty index which is part of the model). WA and MS definitely should not have a large negative correlation.