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Have you ever had a conversation about politics with someone only to realize that whomever you’re talking with has forgotten to calibrate the passion of their opinions with how much they actually know about the world? Of course you have. A classic example that comes to mind is people griping about foreign aid. If you ask how much of the federal budget goes to foreign aid, people will answer that it’s somewhere in the range of 27% – they seem to think around 13% would be more appropriate. The real number is less than 1%. (The same thing, by the way, happens when you’re discussing military spending: people think it’s about a fifth of the federal budget – the real number is less than 5%.)
That should be easy enough to fix: just correct their misinformation. A new study out of the Cultural Cognition Project, however, aims to really depress your expectation of people’s ability to overcome bias and self-correct.
If you want to read the working paper, you can find it here – if not, I’ll just give you a brief overview.
In essence, the study consisted of giving participants one of four basic word problems. The first two of these presented information on the efficacy of a skin cream for treating a given condition. In half of the cases, the data supported one causal inference while in the other half the data supported the opposite inference. Similarly, some participants were given information on whether or not bans on conceal-carry permits lower or raise the crime rate in a city – again, half of the time the data supported the inference that the bans lowered the crime rate, while the other half of the time the data supported the inference that the bans increase the crime rate.
Each participant was presented with one of these four scenarios, and in each case drawing the correct inference from the data required some basic math skills. Participants were ranked in accordance with their level of ‘numeracy’ – or their ability to apply basic numerical concepts. The study also recorded their political affiliation.
Some of the results are entirely predictable. For example, the study found that the politically charged question produced higher levels of polarization – in this case exhibited by the amount of disagreement found within a group over which causal inference was the correct one. (I have to stress that in each group, there was a correct answer: the data the researchers presented was designed that way.)
In addition, participants with better numeracy did a better job of drawing the correct inference from the data – but only on the question about the skin cream. On the question about gun control, the more numerate respondents exhibited higher levels of polarization. This means that the better you were at math, the harder a time you had overcoming your bias in order to solve a basic math problem. Republicans would answer that the data supported the conclusion that bans on carry permits increase the crime rate (even when they demonstrably didn’t), and Democrats would answer that the data supported the conclusion that bans on carry permits lower the crime rate (even when they demonstrably didn’t.)
If you have a basic belief in self-government and think that if we could only get the correct information in front of voters, then we could begin to solve the challenges of polarization – well, then this study is pretty depressing. Or, as the study seems to imply, maybe it isn’t. Maybe you’ve already found a way to apply your superior math skills to the task of not having to change your mind.
On the other hand, at least the study seems to provide some clarity on this point: it isn’t enough to give people better facts to think with – you also have to provide them with a better way to think about those facts. And there’s a message of humility as well – you should always apply a healthy amount of skepticism to your own conclusions. Even more so, it would seem, when you think you reached those conclusions because you’re wicked smart.
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