POLITICS

What The Hogwarts Sorting Hat Would Make Of The Presidential Candidates

"You're a wizard, Bernie."

Political punditry this year has largely focused on sorting the presidential candidates into "establishment" and "outsider" lanes.

Until now, we've only been able to guess at an even more key distinction: who'd be hanging out with Harry Potter in Gryffindor Tower, and who'd be lurking in the Slytherin Dungeon.

Since we lack a real electoral Sorting Hat, we teamed up with the good folks at YouGov to conduct an actual, scientific poll asking the nation (or the 54 percent of it that's familiar with Harry Potter) to step in and make the call.

Of course, some of the Muggle laws of politics still applied. Asked which house they'd choose for themselves, Democrats were most likely to sort themselves into witty, learned Ravenclaw, while Republicans preferred patient, hard-working Hufflepuff. And, rudely, members of both parties sent most of their political opponents to Slytherin. 

Here's where Democrats and Republicans each think their field of presidential candidates belong: 

 The HuffPost/YouGov poll consisted of 1,000 completed interviews conducted Jan. 26-28 among U.S. adults, using a sample selected from YouGov's opt-in online panel to match the demographics and other characteristics of the adult U.S. population.

The Huffington Post has teamed up with YouGov to conduct daily opinion polls.You can learn more about this project and take part in YouGov's nationally representative opinion polling. Data from all HuffPost/YouGov polls can be found here. More details on the polls' methodology are available here.

Most surveys report a margin of error that represents some, but not all, potential survey errors. YouGov's reports include a model-based margin of error, which rests on a specific set of statistical assumptions about the selected sample, rather than the standard methodology for random probability sampling. If these assumptions are wrong, the model-based margin of error may also be inaccurate. Click here for a more detailed explanation of the model-based margin of error. 

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