I'm kind of surprised I haven't seen an article in @FiveThirtyEight or somewhere similar using the Total Survey Error framework as a tool for categorizing potential sources of survey error and tests for those hypotheses as election results get finalized.

When survey researchers hear about "Shy Trump" voters, we hear it as measurement error, and there's good evidence that it's vanishingly small. But I think the broader public might also be including non-response error as part of how they understand that term

As we unpack the sources of survey error, it's worth keeping our eye on some patterns. For instance, this comparison of survey averages to projected results by @gelliottmorris shows a correlation between 2016 and 2020, but also an intercept shift

Putting out some hypotheses now that can be tested as vote counts get finalized (still not done counting!) and pollsters look back at their own data

1) Everything @davidshor says here about trust and non-response: https://t.co/aXYZMc5QO5

It's tricky to test for, because surveys aren't asking about trust and we don't have great national benchmarks either. That said ...