Transcript of
Edmans, A XXXXXXXXXXWhat to trust in a "post-truth" world . TEDx London Business School.
00:05
Belle Gibson was a happy young Australian. She lived in Perth, and she loved
skateboarding. But in 2009, Belle learned that she had
ain cancer and four months to
live. Two months of chemo and radiotherapy had no effect. But Belle was determined. She'd
een a fighter her whole life. From age six, she had to cook for her
other, who had
autism, and her mother, who had multiple sclerosis. Her father was out of the picture. So
Belle fought, with exercise, with meditation and by ditching meat for fruit and vegetables. And
she made a complete recovery.
00:42
Belle's story went viral. It was tweeted, blogged about, shared and reached millions of
people. It showed the benefits of shunning traditional medicine for diet and exercise. In
August 2013, Belle launched a healthy eating app, The Whole Pantry, downloaded 200,000
times in the first month.
01:04
But Belle's story was a lie. Belle never had cancer. People shared her story without ever
checking if it was true. This is a classic example of confirmation bias. We accept a story
uncritically if it confirms what we'd like to be true. And we reject any story that contradicts
it. How often do we see this in the stories that we share and we ignore? In politics, in
usiness, in health advice.
01:38
The Oxford Dictionary's word of 2016 was "post-truth." And the recognition that we now live
in a post-truth world has led to a much needed emphasis on checking the facts. But the
punch line of my talk is that just checking the facts is not enough. Even if Belle's story were
true, it would be just as i
elevant. Why?
02:03
Well, let's look at one of the most fundamental techniques in statistics. It's called Bayesian
inference. And the very simple version is this: We care about "does the data support the
theory?" Does the data increase our belief that the theory is true? But instead, we end up
asking, "Is the data consistent with the theory?" But being consistent with the theory does not
mean that the data supports the theory. Why? Because of a crucial but forgotten third term -
- the data could also be consistent with rival theories. But due to confirmation bias, we never
consider the rival theories, because we're so protective of our own pet theory.
02:50
Now, let's look at this for Belle's story. Well, we care about: Does Belle's story support the
theory that diet cures cancer? But instead, we end up asking, "Is Belle's story consistent with
diet curing cancer?" And the answer is yes. If diet did cure cancer, we'd see stories like
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Belle's. But even if diet did not cure cancer, we'd still see stories like Belle's. A single story in
which a patient apparently self-cured just due to being misdiagnosed in the first place. Just
like, even if smoking was bad for your health, you'd still see one smoker who lived until 100.
03:34
(Laughter)
03:35
Just like, even if education was good for your income, you'd still see one multimillionaire who
didn't go to university.
03:42
(Laughter)
03:47
So the biggest problem with Belle's story is not that it was false. It's that it's only one
story. There might be thousands of other stories where diet alone failed, but we never hear
about them.
04:01
We share the outlier cases because they are new, and therefore they are news. We never
share the ordinary cases. They're too ordinary, they're what normally happens. And that's the
true 99 percent that we ignore. Just like in society, you can't just listen to the one percent, the
outliers, and ignore the 99 percent, the ordinary.
04:25
Because that's the second example of confirmation bias. We accept a fact as data. The
iggest problem is not that we live in a post-truth world; it's that we live in a post-data
world. We prefer a single story to tons of data. Now, stories are powerful, they're vivid, they
ing it to life. They tell you to start every talk with a story. I did. But a single story is
meaningless and misleading unless it's backed up by large-scale data. But even if we had
large-scale data, that might still not be enough. Because it could still be consistent with rival
theories. Let me explain.
05:13
A classic study by psychologist Peter Wason gives you a set of three numbers and asks you
to think of the rule that generated them. So if you're given two, four, six, what's the rule? Well,
most people would think, it's successive even numbers. How would you test it? Well, you'd
propose other sets of successive even numbers: 4, 6, 8 or 12, 14, 16. And Peter would say
these sets also work. But knowing that these sets also work, knowing that perhaps hundreds
of sets of successive even numbers also work, tells you nothing. Because this is still
consistent with rival theories. Perhaps the rule is any three even numbers. Or any three
increasing numbers.
06:05
And that's the third example of confirmation bias: accepting data as evidence, even if it's
consistent with rival theories. Data is just a collection of facts. Evidence is data that supports
one theory and rules out others. So the best way to support your theory is actually to try to
disprove it, to play devil's advocate. So test something, like 4, 12, 26. If you got a yes to that,
that would disprove your theory of successive even numbers. Yet this test is
powerful, because if you got a no, it would rule out "any three even numbers" and "any three
increasing numbers." It would rule out the rival theories, but not rule out yours. But most
people are too afraid of testing the 4, 12, 26, because they don't want to get a yes and prove
their pet theory to be wrong. Confirmation bias is not only about failing to search for new
data, but it's also about misinterpreting data once you receive it.
07:17
And this applies outside the lab to important, real-world problems. Indeed, Thomas Edison
famously said, "I have not failed, I have found 10,000 ways that won't work." Finding out that
you're wrong is the only way to find out what's right.
07:38
Say you're a university admissions director and your theory is that only students with good
grades from rich families do well. So you only let in such students. And they do well. But
that's also consistent with the rival theory. Perhaps all students with good grades do well, rich
or poor. But you never test that theory because you never let in poor students because you
don't want to be proven wrong.
08:06
So, what have we learned? A story is not fact, because it may not be true. A fact is not data, it
may not be representative if it's only one data point. And data is not evidence -- it may not be
supportive if it's consistent with rival theories. So, what do you do? When you're at the
inflection points of life, deciding on a strategy for your business, a parenting technique for
your child or a regimen for your health, how do you ensure that you don't have a story but
you have evidence?
08:47
Let me give you three tips. The first is to actively seek other viewpoints. Read and listen to
people you flagrantly disagree with. Ninety percent of what they say may be wrong, in your
view. But what if 10 percent is right? As Aristotle said, "The mark of an educated man is the
ability to entertain a thought without necessarily accepting it." Su
ound yourself with people
who challenge you, and create a culture that actively encourages dissent. Some banks
suffered from groupthink, where staff were too afraid to challenge management's lending
decisions, contributing to the financial crisis. In a meeting, appoint someone to be devil's
advocate against your pet idea. And don't just hear another viewpoint -- listen to it, as well.
09:44
As psychologist Stephen Covey said, "Listen with the intent to understand, not the intent to
eply." A dissenting viewpoint is something to learn from not to argue against. Which takes us
to the other forgotten terms in Bayesian inference. Because data allows you to learn, but
learning is only relative to a starting point. If you started with complete certainty that your pet
theory must be true, then your view won't change -- regardless of what data you see.
10:20
Only if you are truly open to the possibility of being wrong can you ever learn. As Leo Tolstoy
wrote, "The most difficult subjects can be explained to the most slow-witted man if he has not
formed any idea of them already. But the simplest thing cannot be made clear to the most
intelligent man if he is firmly persuaded that he knows already." Tip number two is "listen to
experts." Now, that's perhaps the most unpopular advice that I could give you.
10:56
(Laughter)
10:57
British politician Michael Gove famously said that people in this country have had enough of
experts. A recent poll showed that more people would trust their hairdresser --
11:08
(Laughter)
11:11
or the man on the street than they would leaders of businesses, the health service and even
charities. So we respect a teeth-whitening formula discovered by a mom, or we listen to an
actress's view on vaccination. We like people who tell it like it is, who go with their gut, and
we call them authentic. But gut feel can only get you so far. Gut feel would