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Election Miscellany #5

A curious thing. There’s a new rush of press stories reporting that Mar-a-Lago is suddenly a bundle of nerves as they see evidence they’re falling short in Pennsylvania. This is certainly why Trump is suddenly going berserk on social media, making freakshow claims that the race is being stolen in PA. We knew that. Meanwhile Trump is suddenly losing ground in betting markets, which for a couple of weeks have shown him to be a prohibitive favorite to return to the White House. This is all very nice to see. But I wouldn’t necessarily see it as some sign of momentum in Harris’s favor.

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Listen To This: Taking Out The Garbage

A new episode of The Josh Marshall Podcast is live! This week, Kate and Josh discuss both campaigns’ closing arguments and the mess at the Washington Post.


You can listen to the new episode of The Josh Marshall Podcast here.

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Election Miscellany #4

If you’re watching the latest polls, make a note of something called “herding.” It could be relevant for discussions of polling after the election. The concept is straightforward. In the final days of an election, poll results tend to trend toward consensus. One possibility is that everyone is finally making up their mind and the picture and reality is coming into focus. But that’s not the only possibility. For a mix of good faith and maybe less than good faith reasons, pollsters can become increasingly leery of publishing an outlier poll. There’s a tendency to “herd” together for extra-statistical reasons.

Let’s say you’re five days out from the election and the polling averages say candidate Jones is up 2 points and you’ve got a poll which says candidate Smith is up 3 points. (Pardon may defaulting to anglo surnames.) Everyone has an outlier result sometimes. But do you really want your final poll to be a weird outlier? In the modern era with aggregators, pollsters are often graded on the predictive accuracy of their final polls. So it kind of matters. If you’re a bit shady maybe you just tweak your numbers and get them closer to the average. If you’re more on the level maybe you take a closer look at the data and find something that really looks like it needs adjusting. Maybe you just decide that you’re going to hold this one poll back.

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Pods

As I mentioned in this week’s podcast, out today, Kate Riga and I are going to be heavying-up on podcasts next week. In addition to the regularly scheduled Wednesday podcast, we are planning to do “instapods” (quick hits lasting 15-20 minutes) through the week. We’re planning on doing the first late on election night. We don’t know precisely when, but sometime late in the evening when we have at least some broad sense of what the results are looking like. And no, we’re not expecting to know a winner at that point. We’re then going to have the regular episode the following afternoon. Then we plan to record late afternoonish instapods on Thursday and Friday afternoons to hit the big developments of the day. If the winner of the election is clear by the following morning, we’re confident there’s still going to be a lot to discuss on Thursday and Friday.

Of course, it’s possible that there will be additional breaking news at any point over the course of the week that might prompt us to do an additional instapod in addition to this schedule.

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How To Analyze the Early Vote

 Member Newsletter

I told you a week or more ago not to try to interpret early voting data yourself. And don’t put much stock in a hot take on it you see from someone on Twitter. It’s a fool’s errand. If you have access to a lot of data you can draw inferences. That can be real-time modeling data the campaigns have access to or it can be various other datasets that provide context for interpreting the data. Even with all that, the hallmark of someone who actually knows what they’re talking about is a lot of tentativeness and uncertainty. With a lot of knowledge you can point to patterns or a tightened ranges of possibilities, not certainties.

I’m doing this post both because the findings are interesting but also because it’s an illustration of how you can actually pull some signals out of the data when you really know your stuff.

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The rhetoric has been a throughline of Trump’s campaign, as he and alumni of his first administration threaten to use the military as a goon squad to crack down on Americans if elected.

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