Shifting Standards: The Tall and the Short of It (Podcast)

Shifting Standards: The Tall and the Short of It (Podcast)


>>OLIVIA KANG:
So, if I’m a participant in this study, what am I seeing?>>MONICA BIERNAT:
Okay. You’re seeing photographs we took of people around Ann Arbor. We tried to take them both standing and sitting
next to some object so that there was some basis for judging.>>OLIVIA KANG:
This is Professor Monica Biernat, University Distinguished Professor of Psychology at the
University of Kansas. She’s describing experiments she conducted
in the 1990s at the University of Michigan. In these studies, her participants do something
simple. They look at pictures of people as they stand
next to doorways and sit near desks – and then guess how tall each person is, in one
of two ways: They either rated how tall the person was on a scale of 1 to 7 (where 1 is
“very short” and 7 is “very tall”), or they had to be specific, and actually guess
each person’s height in feet and inches. Now for the first group, the results were
exactly what they should be. On a 1-7 scale, the tallest men and women
were rated high, the shortest were rated low, and men and women were equally likely to be
seen as tall, average, or short. No surprises there. But when people had to guess height in feet
and inches, their answers changed… and presented the first evidence of a phenomenon we now
know as shifting standards. Welcome to Outsmarting Human Minds.>>OLIVIA KANG
What are shifting standards?>>MONICA BIERNAT
So the basic idea is a pretty simple one. It’s that when you make any sort of a judgment:
how big an object is; how nice a person is, you might use different standards depending
on who you’re judging. We’re going to judge people relative to
within-group standards. So, you know, I say, ‘My cat is huge!’
but no one’s surprised that my big cat can fit in a small car because we know that ‘small’
and ‘big’ means something different depending on what’s being described. So it is, I think, I think, a really natural
process.>>OLIVIA KANG:
Professor Biernat’s study showed just how strong this “natural process” is: Remember, when people in the study simply
rated height on a 1-7 scale, they showed no bias. Men and women were equally likely to be called
“tall”, “short”, and anything in between. But when the researchers asked them to guess
exactly how tall… something shifted. Participants might call both a man and woman
“tall”… but they were consistently guessing that the men were several inches taller than
the women. Depending on who they were looking at, “tall”
could mean 5’9” or 6’1”. When I first heard this, I thought: “So
what? Everyone knows that men, on average, are taller
than women.” But then, Monica said this:>>MONICA BIERNAT
I should say men actually were not taller than women in the sample.>>OLIVIA KANG:
For every woman who was 5’3”, researchers made sure there was a man who was 5’3”. For every man who was 6’2”, there was
a woman who was 6’2”. And participants knew it. Here are some of the instructions they heard:>>MALE NARRATOR:
“In this booklet, the men and women are actually of equal height. We have taken care to match the heights of
the men and women pictured … Therefore, in order to make as accurate a height judgment
as possible … do not rely on the person’s sex.”>>OLIVIA KANG:
The researchers did one more thing to motivate everyone to guess height correctly: they offered
cold, hard cash. Be accurate, and win an extra $50. It shouldn’t have been too hard: after all,
the photos held clues: if Benjamin and Maria both come up to the same place on a door-frame,
they’d have to be the same height! But none of this made much difference. Participants still saw the men as significantly
taller than the women. This is why the data are so striking. Not because people used gender stereotypes
to make their judgments. Of course they did – we all would. The real surprise is that people couldn’t
let these stereotypes go. Even with explicit instructions, even with
data in front of them and money on the line, they couldn’t get it right. Let’s be clear: there are times when we
should shift our standards. I mean, why would you want to assume that
a big cat is larger than a Mini Cooper, or that a “brilliant” 3 year-old can do particle
physics? The difficulty is that we also shift our standards
when we shouldn’t. When adjusting actually leads to error, like
we see in the height study. So how do we know when it’s okay for our
standards to shift? It all boils down to two simple questions
we can ask ourselves: One: Are you confident in whatever belief
is causing this shift? I mean, how big can cats really get? And two: Does the data back you up in this
particular case? Take the height study. Are men taller than women? On average, yes, of course. In every culture, everywhere on the planet. But in the sample of men and women in front
of you, does the data back you up? Actually, no. Not in the Biernat study. So we have to revise our stereotypes.>>OLIVIA KANG:
You know, this, this reminds me of: I think when I was in 6th grade, a lot of people would
talk about how, you know, “At this moment, the best golfer is Tiger Woods, the best rapper
is Eminem, the tallest basketball player is Yao Ming”, and how these all went against
stereotypes.>>MONICA BIERNAT:
How funny! Uh-huh … that is the case where
the objective evidence is sufficient to push people over the top.>>OLIVIA KANG:
So it’s important to consider the objective evidence in front of you. Because here’s the thing. Not all the patterns we learn are right. Sure, it’s silly for us to expect a cat
to be bigger than a car. But we’ve also learned other patterns: stereotypes about what CEOs, golfers,
and surgeons look like; what women, individuals with disabilities,
or the elderly are capable of. And these patterns we learn can distort what
we see. Professor Biernat’s studies suggest the
kinds of long-term consequences shifting standards can have in the workplace. Imagine you’re an employee for whom the
bar was set low. That makes it easy to exceed expectations. Monica shows that this will lead to more praise…
but that this praise is usually empty.>>MONICA BIERNAT:
So, you might get positive feedback about your performance: ‘Wow, that was great,
you did a really good job’ – but down the road, when a bottom-line decision is being
made about ‘Well, who am I gonna give the best assignment to?’ or ‘Who am I gonna
hire?’, ‘Who am I gonna promote?’ – those judgments are more likely to be
consistent with the broader stereotype.>>OLIVIA KANG: One of my colleagues calls this
getting “a pat on the head, and then a kick in the pants”. And over time, the effects of this can build
up, creating confusion and a lack of motivation.>>MONICA BIERNAT:
And that can also breed the sense of: “Can I trust any feedback that I get? What does it mean if somebody says ‘Great
job!’ but then doesn’t, give me the next plum assignment? I can’t trust anything, maybe, that this
person says” and that can create another sort of cycle that is bad for everybody.>>OLIVIA KANG:
Of course, no one wants this. As managers, shifting standards can cause
us to overlook talent that’s right in front of us. And on the other side, it can leave talented
employees wondering, “Why even try?” So what can we do to keep our standards steady? We’ve already talked about two questions
you can use to stay on track: One: Ask yourself if what you believe is fact. Don’t just rely on your sense of things
or what something feels like. If you do, you’re more likely to rely on
your stereotypes –>>MONICA BIERNAT:
So, for example, assuming that a woman who’s pregnant will not want to take on a new case
– is probably not the thing you should do; you should ask the person if this is what
they want.>>OLIVIA KANG:
Two: Ask yourself if this belief is supported by
relevant data.>>MONICA BIERNAT:
You know, when it comes to evaluating performance, again, have specific criteria, focus on performance
in a particular time window.>>OLIVIA KANG: And there’s a third
thing we can watch out for: Language.>>MONICA BIERNAT:
Because natural language of evaluation is subjective, it doesn’t necessarily mean
what people think it means. ‘Good’ doesn’t necessarily mean you’re
objectively good.>>OLIVIA KANG:
So ask yourself: what does it mean to be “aggressive” or “a visionary”? What are the metrics for vague terms like
“executive presence”? Can we back up the words we’re using with
standards we hold steady for everyone? What we’re trying to fix here is not easy. But the tall and the short of it is: we can
seek out the right data, ask the right questions, and if we do, we could make better decisions. Outsmarting Human Minds is a program founded
by Mahzarin Banaji, devoted to improving decision-making using insights from psychological science. Support for this episode was provided by PwC
and Harvard University. This episode was developed by Olivia Kang
and Mahzarin Banaji, and featured Professor Monica Biernat. Sound editing and mixing was done by Evan
Younger. Music was composed by Miracles of Modern Science. For references and related materials, go to
outsmartinghumanminds.org.

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