The Availability Bias

MAHZARIN BANAJI: So, let me start by giving you an example of a very simple experiment.

NARRATOR: Welcome to Outsmarting Implicit Bias. You’re listening now to Professor Mahzarin Banaji.

MAHZARIN BANAJI: It was done at the University of Michigan a long time ago now, and here’s what they did: They said to half the people in their experiment: “Please write down 3 reasons for why it is that you love your partner,” okay? The other group is told something very similar: write down for us 9 reasons for why you love your partner …

Imagine doing this, and then at the end of this very simple exercise, you’re asked a few questions: How happy are you in this relationship? How much do you love this person? … And so on as a measure of the intensity and the depth of your love.

The data show that those of you who wrote down 3 reasons for your love ended up expressing much greater happiness and satisfaction with your relationship than those of you poor people on this side who wrote down 9 reasons for your love. Why should this be?

Well, the answer is actually kind of simple from the point of view of this 3-pound organ between your ears. The answer is: Who has 9 good qualities? This is not an easy thing to do at all!

And so, think about what your brain did. For you, 3 was easy to write down. But your brain wasn’t just writing down those three reasons as asked to do; it’s computing a million other things. Among them: how easy is this?

And at the end of the writing down of the three, if it felt easy to you, that actually influences how much happiness you express. But you guys on this side, think about your job. By the time you get to #6, your hand is slowing down, and your brain notices that. And you know that you just made up 7, 8, and 9 – they’re not even true! And it didn’t let go of that, it said this was hard, maybe I’m not so happy, maybe we should see a marriage counselor.

NARRATOR: This is an example of the availability bias, first discovered by psychologists Amos Tversky and Daniel Kahneman. It’s a blindspot that tricks us into thinking that the more easily something comes to mind, the more true or frequent it must be.

You just heard an example of how this bias can warp our perceptions of truth: how happy we are in our relationships. But it doesn’t end there: the same researchers (led by Norbert Schwarz) showed that the availability bias can even change how we view our selves. Many of us think we have a good grasp of who we are: whether we’re confident, kind, happy, ambitious. But challenge yourself to come with 12 examples of times you behaved assertively, for example. The science predicts you’ll believe you’re less assertive than if you only had to come up with 6. Strange but true.

Now apply this idea of “availability” to how often you think things happen in the world. For example: what’s more likely? Death by shark attack or death by lightning strike? The science suggests you’ll choose “shark attacks”. It’s the wrong answer… but it makes sense. Shark attacks get more media coverage; they’re the subject of blockbuster movies. So these examples stick and come to mind more easily. And that makes our brains think “Must happen a lot”.

Now, how does this blurred line between what’s true and what’s available affect us as work?

MAHZARIN BANAJI: Several years ago, I was listening to the senior-most members of an organization discuss a problem with dread in their voices. This is what they said: we hire women in their … 20s, but then (and their voices lowered as they spoke), the biological clock, you know…

NARRATOR: The question the company was facing was: how could they navigate these replacement costs, and was it worth the investment? But then the question became moot.

MAHZARIN BANAJI: I was looking at their data, and to my great surprise, men and women were actually leaving at the same rate. And the first reason both groups gave for leaving? To work for a competitor.] The leaders were shocked. How did the truth get so twisted?

MAHZARIN BANAJI: The availability bias.

NARRATOR: This belief is a common one: women leaving to raise children is something we hear about, something that managers might worry about… so when it does happen, it sticks. And then… you know the rest. Clearly this mental shortcut doesn’t always lead us to the right answer. So how do we outsmart it?

MAHZARIN BANAJI: The solution is so simple I’m embarrassed to even propose it to you: look at the data that are right in front of you!

NARRATOR: And this applies to everyone. A recent global survey by the ICEDR of hundreds of millennials found that men and women around 30 leave work at the same rates, for the same reasons. The data are there. They can help us to sift what’s true from what’s simply available. We just have to look.

Outsmarting Implicit Bias is a project founded by Mahzarin Banaji, devoted to improving decision-making using insights from psychological science. The team includes Olivia Kang, Kirsten Morehouse, Evan Younger, and Mahzarin Banaji. Special thanks to Miracles of Modern Science for music. Support comes from Harvard University, PwC, and Johnson & Johnson.