🚨Out today in PNAS @PNASNews🚨. Why do people overestimate the size of politically relevant groups (immigrant, LGBTQ, Jewish) and quantities (% of budget spent on foreign aid, % of refugees that are criminals)?. We analyze 100k estimates to find out🧵👇

RT @TaniaLombrozo: Laypeople often learn about science from expert explanations & those explanations often contain JARGON. Does jargon make….

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RT @adamrpanish: 🚨 My first solo-authored article is out now at @BJPolS 🚨. tl;dr: When people w/ high verbal ability consume info, their id….

Thanks to my fantastic co-authors @TylerMarghetis @DrDavidLandy and Cara Wong, and everyone who gave us feedback over many years. Ungated earlier version of the paper here:

Our findings suggest that the public knows more about politics than we give them credit for:. People make errors when estimating politically-relevant percentages, but this is due to the format of the question not underlying misinformation about what they are estimating.

Of course, characteristics of specific groups may matter, but only at the margins. We should first account for the domain-general errors people make *anytime* they estimate a percentage, then examine group-specific explanations.

The same is true of theories that people overestimate the size of groups that they have a lot of social contact with. Very little evidence of this!.

We also test popular theories that people overestimate the size of groups they fear. Not the case. Again, misestimates result mainly from the psychological errors we make anytime we estimate %s, not from anything specific to the group being estimated

We argue that this pattern of over-under estimation arises from 🧠Bayesian reasoning under uncertainty🧠: people often have uncertain ideas in their minds about the size of these groups, but when they convert these ideas to percentages they ‘hedge’ their estimates toward a prior.

And this is the same pattern of errors people make when estimating things like the percentage of dots on a page that are red 👇

Here’s the key figure: people make the same estimation errors regardless of what they are estimating---political and *entirely non-political* quantities. These are 100k estimates of the size of racial and non-racial groups made by 37k people in 22 countries

Instead, people are just really bad at estimating percentages. They systematically overestimate smaller %s and underestimate larger %s, including ENTIRELY NON-POLITICAL %s, such as the % of the population that owns an Apple product, has a passport, or has indoor plumbing.

We argue that journalists and academics are *wrong* when they interpret these misperceptions as evidence that the public is ignorant and misinformed 👇

RT @ROrchinik: New WP!.The illusory truth effect (repetition -> belief) is core to psych of beliefs, & thought to be a deep bias impacting….

I'm very happy to share that I'll be joining the Department of Political Science at @UNC Chapel Hill as an Assistant Professor this fall. I'm excited for this next chapter and will always be incredibly grateful for my amazing experience at Stony Brook.

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RT @YamilRVelez: 🚨How should we design surveys to capture opinion in fast-changing contexts or within hard-to-reach communities? 🚨. My pape….