Predicting the 2020 Democratic Primary is Stupid | Pt 2. Correlation is not Causation
Since 1980, the Iowa Caucus has correctly predicted the eventual Democratic Nominee every time except once, in 1992.
Of those Democratic Nominee predictions, 3 won the popular the vote (Al Gore, Barack Obama and Hillary Clinton). The rest were crushed in electoral landslides (Walter Mondale, Mike Dukakis, John Kerry).
Of the 3 who won the popular vote, only 1 became President, Barrack Obama.
The year that Iowa incorrectly predicted the nominee, 1992, produced a President who had a plurality of the popular vote, but who still became President — Bill Clinton.
So, since 1980, Iowa has correctly predicted almost every Democratic Nominee, but has only correctly predicted the President once. What does all of this mean?
It means absolutely nothing. Zero. Correlation isn’t causation.
Turnout Determines Elections
Sometimes things can look like patterns that aren’t there. Your judgements about people’s behaviors can be off by an inch or a mile, depending on how you group them. Your ability to understand data about people’s behavior depends a lot on how well you group people, and how well you can guess about the behavior of the people you grouped together. It’s imprecise and messy, and in politics, no one is really doing much of that kind of work. A lot of political pundits create the illusion that they’re looking at data, when most of them are just riffing off the top of their heads on TV as a guest, and look smart while doing it.
I do know one thing about political data, though, the one that (to me) feels like it gets ignored the most.