Lessons in #UX: Election 2016 and confirmation bias

So, anything interesting happen this Tuesday?

OK, lame joke, and I understand a lot of people are very upset and shocked by the outcome of the presidential election. I was too… But not nearly as much as many people are. Why? Because I was paying attention to WHY people were voting, and trust me… it wasn’t obvious. Especially when it comes to people who voted for Trump.

While I am not a mind reader, A casual browse of any conservative blogs would tell you that the base that showed up for Trump was angry. Not at anyone because of their race, creed or color (though, yes, there were some people who were motivated by that – though I think the “Alt-right” that was a very small minority). They were angry because they felt that the government was not looking after their best interests. A tremendous number of people in America are hurting, with high “real” unemployment and rising health insurance and health costs. They think that establishment is not on their side. And Trump was/is.

Michael Moore saw it. Other people saw it.

And most of the mainstream media did not.

Why? Two simple words. “Confirmation bias.” Most reporters were/are (admittedly) left-leaning and they view the world through that prism. They look for evidence to confirm and validate their world view, and see any data point that confirms this as MUCH more important than other, potentially CONFLICTING, data points.

(Yes, they claim they are unbiased, and I know many try… but we are imperfect creatures, and bias creeps in… even subconsciously. Nobody is perfect)

Some of the biases I saw in some reporting (stated or underlying) were: Republican’s won’t show up for Trump, because he did such horrible things! No women would vote for him! All his supporters are racist! All minorities will shun him! And so on and so on and…

I am just as guilty of this as anyone, but as a user researcher I have learned to try to avoid confirmation bias in my work. You interview people, you don’t ask loaded questions, you listen and observe. You draw your conclusions based on evidence and observation. Many reporters asked loaded questions, and focused on the worse parts of the Trump movement to showcase and highlight – reinforcing their worldview through their reporting.

They create a narrative, and they reinforce the narrative. And many of the people who watch believed in this narrative. The overwhelming narrative over the past month? Hillary was going to win. It was no contest.

Scott Adams (creator of Dilbert, and a man who predicted Trump would win 14 MONTHS AGO) talks about this here. There are basically 2 universes, and both are equally “right” to the people inhabiting it.

Because of their bias the media did their viewers and voters a disservice. If they had actually bothered to be intellectually curious, and really investigate WHY Trump supporters supported him and were so angry, and started looking at the enthusiasm and size of the rally (especially compared to Clinton’s) then they may have changed their polling models… and this in turn may have lead to more balance and accuracy in their predictions and reporting.

And guess what? If the reality of the situation are accurately reported it would have encouraged more people to vote for Clinton… and she could have won. Lots of young people (who have their own confirmation bias, and therefore look to information sources that reinforce their world view) saw the “Hillary can’t lose” narrative in story after story and so… they didn’t vote. These millennials watched CNN, looked at all the social-media posts from their like-minded friends, and believed she “had it.”

Well, about that…

Lots of navel-gazing is going on, and the following articles are good things to read to get a well-rounded sense of why things happened the way it did. And Definitely read as much of Scott Adams’ blog as you can, because he has been more right than not. For MONTHS.

Joseph Dickerson is a user experience professional and UX Lead for Microsoft based out of Atlanta, GA. He has implemented processes in user testing, design and ethnographic research and provided design and consulting services for many different projects and organizations.

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