FiveThirtyEight: Can You Trust Trump’s Approval Rating Polls?

That third possibility is pretty much exactly what happened. Trump beat the final FiveThirtyEight national polling average by only 1.8 percentage points. Meanwhile, he beat the final FiveThirtyEight polling average in the average swing state — weighted by its likelihood of being the tipping-point state — by 2.7 percentage points. (The miss was larger than that in Wisconsin, Michigan and Pennsylvania, but Clinton met or slightly exceeded her polls in several other swing states.) This was nothing at all out of the ordinary. The polls were about as accurate as they’d been, on average, in presidential elections since 1968. They were somewhat more accurate than they’d been in the most recent federal election, the 2014 midterms. But they were enough to tip the election to Trump because Clinton had been in a precarious position to begin with. [...]

There’s one other critical distinction that people often miss. The margin of error, as traditionally described, applies only to one candidate’s vote share (“Clinton has 47 percent of the vote”) or one side of a yes/no question (“41 percent of voters approve of Trump’s performance”). The margin of error for the difference between two candidates (“Clinton leads Trump by 5 percentage points”) — or a candidate’s net approval rating (“Trump has a negative-10 approval rating”) — is roughly twice as high: [...]

But so many of the articles I read toward the end of last year’s campaign didn’t convey any sense of uncertainty at all. A small Clinton lead was misreported as a sure thing. And then a small polling error was misreported as a massive failure of the data. It’s a fairly minor part of the puzzle, but if journalists want to rebuild trust in their reporting, ending the boom-and-bust cycle in how they report on polling — first overrating its precision and then being shocked when it’s even a couple of percentage points off — would be one way to start. Doing so would make it harder for Trump, or other politicians, to undermine confidence in polls they don’t like.

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