You’d think we’d be able to tell the difference between a highly diverse set of colored T-shirts and a highly diverse set of team members. But a recent study out of the Stanford Graduate School of Business found that, when we try to evaluate diversity, our brains often confuse one type for another.
According to the study, when we perceive a group as diverse in one respect, we tend to think of that group as more diverse in another dimension, even when that’s not the case.
In one experiment, for example, researchers presented participants with a series of pictures of three people. Each group had the exact same gender makeup—two men and one woman—but some trios were more racially diverse than others. When asked to evaluate the gender diversity of the groups, participants said the more racially diverse photos were more gender diverse, even though the number of men and women hadn’t actually changed.
Surprisingly, the study also found this tendency occurs even when one type of diversity is relatively mundane—like a group of people wearing different colored T-shirts—and the other is something we tend to take more seriously—like differences in gender or race.
David Daniels, the study’s lead author, said that what explains this “spillover bias” is heuristics, or mental shortcuts we use to make judgments and decisions quickly. Daniels said that these shortcuts can be useful, reasonable and, oftentimes, correct. For example, you might reasonably assume that a more rebellious person and a more mainstream person that have different tastes in music might have tastes that differ in other ways as well.
“So it’s not that these kind of inferences are always wrong,” Daniels said. “But there is a problem because they’re not always going to be right. And so we systematically make certain types of inference over and over again, and sometimes those inferences aren’t justified by the actual environment that we’re around.”
And those incorrect inferences can have real world implications, particularly when it comes to decision making within organizations or teams. In another experiment, when Daniels’ team offered participants a monetary bonus to choose a group of programmers with a diverse set of skills, participants didn’t make the decision based on which group knew the most programming languages. They chose the group with the most racial and gender diversity.
I think that’s one example of how this might play out in the real world,” Daniels said. “If you’re looking for one type of diversity, then you might find yourself inadvertently seeking out other types or thinking that other types of diversity can sort of fulfill your original need, even when there’s no logical way that’s true.”
Daniels also said that spillover bias might explain the lack of demographic diversity in certain industries, like the well-known lack of gender diversity in Silicon Valley tech companies.
“What we suspect might be happening is that sometimes people are over-extrapolating,” he said. “They see the presence of certain types of diversity around them—such as birthplace, where you grew up, where you went to school, whether you like Star Trek or Star Wars, or something like that—they see these types of diversities and they infer, ‘Well, we have a diverse organization.'”
Part of the problem, Daniels said, is that people rely heavily on their intuitions to make decisions or assessments about diversity. And while spillover bias can be difficult to overcome, there are a few things that managers can do to more accurately evaluate diversity within their team.
Being more intentional is key. Daniels said managers should think carefully about what dimension of diversity is needed according to the problems their teams are facing. After that, he recommends that managers ask themselves two questions when assessing diversity: “Is my team diverse on the dimension that I care about?” and “Does my data back that up?”
“If you ask yourself those two questions, your evaluations of diversity will be much more accurate,” he said. “Because you’re asking the right question first. And second, if you’re asking it in a way that you’re looking for data-driven answers, that will prevent your intuitions from going too far astray.”
Briana Flin is Rewire’s multimedia intern and a soon-to-be second year student at the UC Berkeley Graduate School of Journalism. Before studying new media at Berkeley, Briana was the communications manager at a national education nonprofit. When she’s not busy telling digital stories, she can most likely be found making (and eating) ice cream, rewatching Mad Men or reading any article she can get her hands on. Reach her via email at email@example.com. Follow her on Twitter @BrianaFlin and on Instagram @briana.flin.