Nate Cohn has a must-read for those following Senate elections. He notices that the states that are most important for control of the Senate are also those with the least reliable polling.
Among other things, he notices a surge in inexpensive, probably not very reliable partisan polls in those states. Combined with a dearth of non-aligned polling and some state-specific issues, this makes interpretation quite difficult. Adjust accordingly: Be wary of any conclusions about Alaska, Louisiana, Colorado, Arkansas and North Carolina.
Unfortunately, the reaction of the usually astute Chuck Todd is to throw out the baby with the bathwater: He tweeted: "Reason #5,324 why averaging polls is useless, now more than ever." Well, no. Polling averaging is sound. Sophisticated polling aggregation, which can assess how much weight (if any) to assign to less reliable polling and otherwise correct for detectable biases, is even more important, not less, when the polls may be misleading.
Granted: the specific choices of aggregators aren't always obvious. Take partisan polls. Should they be excluded altogether? Included, but given less weight? Included, but adjusted? And if included, should the adjustments or weights be universal (all partisan pollsters get the same treatment) or pollster-specific (which sounds nice, but what if a pollster doesn't have an established track record)? Fortunately, different aggregators use slightly different methods, and we don't have to choose between them; an overview can give us a pretty good sense of an election.
However, dropping poll averaging altogether, as Todd suggests, hardly solves the problem. Even if we had a single, gold-standard pollster, and even if we all could agree on who that was, we would still be subject to the kinds of standard random errors that are inherent in even the best polls. Given those constraints, we're far better off averaging what we have. Like it or not, the math works.
Meanwhile: nice catch!
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