What to trust in a "post-truth" world
-
0:02 - 0:05Belle Gibson was a happy young Australian.
-
0:05 - 0:08She lived in Perth,
and she loved skateboarding. -
0:08 - 0:13But in 2009, Belle learned that she had
brain cancer and four months to live. -
0:13 - 0:17Two months of chemo
and radiotherapy had no effect. -
0:17 - 0:19But Belle was determined.
-
0:19 - 0:21She'd been a fighter her whole life.
-
0:21 - 0:24From age six, she had to cook
for her brother, who had autism, -
0:24 - 0:27and her mother,
who had multiple sclerosis. -
0:27 - 0:28Her father was out of the picture.
-
0:29 - 0:32So Belle fought, with exercise,
with meditation -
0:32 - 0:35and by ditching meat
for fruit and vegetables. -
0:35 - 0:38And she made a complete recovery.
-
0:39 - 0:40Belle's story went viral.
-
0:40 - 0:44It was tweeted, blogged about,
shared and reached millions of people. -
0:44 - 0:47It showed the benefits of shunning
traditional medicine -
0:47 - 0:49for diet and exercise.
-
0:49 - 0:54In August 2013, Belle launched
a healthy eating app, -
0:54 - 0:55The Whole Pantry,
-
0:55 - 0:59downloaded 200,000 times
in the first month. -
1:01 - 1:04But Belle's story was a lie.
-
1:05 - 1:07Belle never had cancer.
-
1:08 - 1:12People shared her story
without ever checking if it was true. -
1:13 - 1:16This is a classic example
of confirmation bias. -
1:16 - 1:21We accept a story uncritically
if it confirms what we'd like to be true. -
1:21 - 1:24And we reject any story
that contradicts it. -
1:25 - 1:27How often do we see this
-
1:27 - 1:30in the stories
that we share and we ignore? -
1:30 - 1:34In politics, in business,
in health advice. -
1:35 - 1:39The Oxford Dictionary's
word of 2016 was "post-truth." -
1:40 - 1:43And the recognition that we now live
in a post-truth world -
1:43 - 1:47has led to a much needed emphasis
on checking the facts. -
1:47 - 1:49But the punch line of my talk
-
1:49 - 1:52is that just checking
the facts is not enough. -
1:52 - 1:55Even if Belle's story were true,
-
1:55 - 1:57it would be just as irrelevant.
-
1:58 - 2:00Why?
-
2:00 - 2:03Well, let's look at one of the most
fundamental techniques in statistics. -
2:03 - 2:06It's called Bayesian inference.
-
2:06 - 2:09And the very simple version is this:
-
2:09 - 2:12We care about "does the data
support the theory?" -
2:13 - 2:17Does the data increase our belief
that the theory is true? -
2:18 - 2:22But instead, we end up asking,
"Is the data consistent with the theory?" -
2:23 - 2:25But being consistent with the theory
-
2:25 - 2:28does not mean that the data
supports the theory. -
2:29 - 2:30Why?
-
2:30 - 2:34Because of a crucial
but forgotten third term -- -
2:34 - 2:37the data could also be consistent
with rival theories. -
2:38 - 2:43But due to confirmation bias,
we never consider the rival theories, -
2:43 - 2:46because we're so protective
of our own pet theory. -
2:47 - 2:49Now, let's look at this for Belle's story.
-
2:49 - 2:53Well, we care about:
Does Belle's story support the theory -
2:53 - 2:55that diet cures cancer?
-
2:55 - 2:57But instead, we end up asking,
-
2:57 - 3:01"Is Belle's story consistent
with diet curing cancer?" -
3:02 - 3:03And the answer is yes.
-
3:04 - 3:08If diet did cure cancer,
we'd see stories like Belle's. -
3:09 - 3:12But even if diet did not cure cancer,
-
3:12 - 3:14we'd still see stories like Belle's.
-
3:15 - 3:20A single story in which
a patient apparently self-cured -
3:20 - 3:23just due to being misdiagnosed
in the first place. -
3:24 - 3:27Just like, even if smoking
was bad for your health, -
3:27 - 3:30you'd still see one smoker
who lived until 100. -
3:31 - 3:32(Laughter)
-
3:32 - 3:35Just like, even if education
was good for your income, -
3:35 - 3:39you'd still see one multimillionaire
who didn't go to university. -
3:39 - 3:44(Laughter)
-
3:44 - 3:48So the biggest problem with Belle's story
is not that it was false. -
3:48 - 3:51It's that it's only one story.
-
3:51 - 3:55There might be thousands of other stories
where diet alone failed, -
3:55 - 3:57but we never hear about them.
-
3:58 - 4:02We share the outlier cases
because they are new, -
4:02 - 4:04and therefore they are news.
-
4:05 - 4:07We never share the ordinary cases.
-
4:07 - 4:10They're too ordinary,
they're what normally happens. -
4:11 - 4:14And that's the true
99 percent that we ignore. -
4:14 - 4:17Just like in society, you can't just
listen to the one percent, -
4:17 - 4:18the outliers,
-
4:18 - 4:21and ignore the 99 percent, the ordinary.
-
4:22 - 4:25Because that's the second example
of confirmation bias. -
4:25 - 4:28We accept a fact as data.
-
4:29 - 4:33The biggest problem is not
that we live in a post-truth world; -
4:33 - 4:37it's that we live in a post-data world.
-
4:38 - 4:42We prefer a single story to tons of data.
-
4:43 - 4:46Now, stories are powerful,
they're vivid, they bring it to life. -
4:46 - 4:48They tell you to start
every talk with a story. -
4:48 - 4:49I did.
-
4:50 - 4:54But a single story
is meaningless and misleading -
4:54 - 4:57unless it's backed up by large-scale data.
-
4:59 - 5:02But even if we had large-scale data,
-
5:02 - 5:04that might still not be enough.
-
5:04 - 5:07Because it could still be consistent
with rival theories. -
5:08 - 5:09Let me explain.
-
5:10 - 5:13A classic study
by psychologist Peter Wason -
5:13 - 5:15gives you a set of three numbers
-
5:15 - 5:18and asks you to think of the rule
that generated them. -
5:19 - 5:23So if you're given two, four, six,
-
5:23 - 5:24what's the rule?
-
5:25 - 5:28Well, most people would think,
it's successive even numbers. -
5:29 - 5:30How would you test it?
-
5:30 - 5:34Well, you'd propose other sets
of successive even numbers: -
5:34 - 5:374, 6, 8 or 12, 14, 16.
-
5:38 - 5:40And Peter would say these sets also work.
-
5:41 - 5:44But knowing that these sets also work,
-
5:44 - 5:48knowing that perhaps hundreds of sets
of successive even numbers also work, -
5:49 - 5:50tells you nothing.
-
5:51 - 5:54Because this is still consistent
with rival theories. -
5:55 - 5:58Perhaps the rule
is any three even numbers. -
5:59 - 6:01Or any three increasing numbers.
-
6:02 - 6:05And that's the third example
of confirmation bias: -
6:05 - 6:09accepting data as evidence,
-
6:09 - 6:12even if it's consistent
with rival theories. -
6:13 - 6:16Data is just a collection of facts.
-
6:16 - 6:21Evidence is data that supports
one theory and rules out others. -
6:23 - 6:25So the best way to support your theory
-
6:25 - 6:29is actually to try to disprove it,
to play devil's advocate. -
6:29 - 6:34So test something, like 4, 12, 26.
-
6:35 - 6:39If you got a yes to that,
that would disprove your theory -
6:39 - 6:41of successive even numbers.
-
6:41 - 6:43Yet this test is powerful,
-
6:43 - 6:48because if you got a no, it would rule out
"any three even numbers" -
6:48 - 6:50and "any three increasing numbers."
-
6:50 - 6:53It would rule out the rival theories,
but not rule out yours. -
6:54 - 6:59But most people are too afraid
of testing the 4, 12, 26, -
6:59 - 7:03because they don't want to get a yes
and prove their pet theory to be wrong. -
7:05 - 7:10Confirmation bias is not only
about failing to search for new data, -
7:10 - 7:14but it's also about misinterpreting
data once you receive it. -
7:14 - 7:18And this applies outside the lab
to important, real-world problems. -
7:18 - 7:21Indeed, Thomas Edison famously said,
-
7:21 - 7:23"I have not failed,
-
7:23 - 7:27I have found 10,000 ways that won't work."
-
7:28 - 7:31Finding out that you're wrong
-
7:31 - 7:34is the only way to find out what's right.
-
7:35 - 7:38Say you're a university
admissions director -
7:38 - 7:40and your theory is that only
students with good grades -
7:40 - 7:42from rich families do well.
-
7:42 - 7:45So you only let in such students.
-
7:45 - 7:46And they do well.
-
7:46 - 7:49But that's also consistent
with the rival theory. -
7:50 - 7:52Perhaps all students
with good grades do well, -
7:52 - 7:54rich or poor.
-
7:54 - 7:58But you never test that theory
because you never let in poor students -
7:58 - 8:01because you don't want to be proven wrong.
-
8:03 - 8:04So, what have we learned?
-
8:05 - 8:09A story is not fact,
because it may not be true. -
8:09 - 8:12A fact is not data,
-
8:12 - 8:16it may not be representative
if it's only one data point. -
8:17 - 8:19And data is not evidence --
-
8:19 - 8:23it may not be supportive
if it's consistent with rival theories. -
8:24 - 8:26So, what do you do?
-
8:27 - 8:30When you're at
the inflection points of life, -
8:30 - 8:33deciding on a strategy for your business,
-
8:33 - 8:35a parenting technique for your child
-
8:35 - 8:38or a regimen for your health,
-
8:38 - 8:41how do you ensure
that you don't have a story -
8:41 - 8:43but you have evidence?
-
8:44 - 8:46Let me give you three tips.
-
8:47 - 8:51The first is to actively seek
other viewpoints. -
8:51 - 8:54Read and listen to people
you flagrantly disagree with. -
8:54 - 8:58Ninety percent of what they say
may be wrong, in your view. -
8:59 - 9:01But what if 10 percent is right?
-
9:02 - 9:03As Aristotle said,
-
9:03 - 9:06"The mark of an educated man
-
9:06 - 9:09is the ability to entertain a thought
-
9:09 - 9:11without necessarily accepting it."
-
9:13 - 9:15Surround yourself with people
who challenge you, -
9:15 - 9:19and create a culture
that actively encourages dissent. -
9:19 - 9:22Some banks suffered from groupthink,
-
9:22 - 9:26where staff were too afraid to challenge
management's lending decisions, -
9:26 - 9:28contributing to the financial crisis.
-
9:29 - 9:33In a meeting, appoint someone
to be devil's advocate -
9:33 - 9:35against your pet idea.
-
9:36 - 9:38And don't just hear another viewpoint --
-
9:38 - 9:40listen to it, as well.
-
9:41 - 9:44As psychologist Stephen Covey said,
-
9:44 - 9:47"Listen with the intent to understand,
-
9:47 - 9:49not the intent to reply."
-
9:50 - 9:53A dissenting viewpoint
is something to learn from -
9:53 - 9:55not to argue against.
-
9:56 - 10:00Which takes us to the other
forgotten terms in Bayesian inference. -
10:00 - 10:03Because data allows you to learn,
-
10:03 - 10:06but learning is only relative
to a starting point. -
10:06 - 10:12If you started with complete certainty
that your pet theory must be true, -
10:12 - 10:14then your view won't change --
-
10:14 - 10:16regardless of what data you see.
-
10:17 - 10:21Only if you are truly open
to the possibility of being wrong -
10:21 - 10:22can you ever learn.
-
10:24 - 10:26As Leo Tolstoy wrote,
-
10:26 - 10:28"The most difficult subjects
-
10:28 - 10:31can be explained to the most
slow-witted man -
10:31 - 10:34if he has not formed
any idea of them already. -
10:34 - 10:36But the simplest thing
-
10:36 - 10:39cannot be made clear
to the most intelligent man -
10:39 - 10:43if he is firmly persuaded
that he knows already." -
10:44 - 10:48Tip number two is "listen to experts."
-
10:49 - 10:53Now, that's perhaps the most
unpopular advice that I could give you. -
10:53 - 10:54(Laughter)
-
10:54 - 10:59British politician Michael Gove
famously said that people in this country -
10:59 - 11:01have had enough of experts.
-
11:02 - 11:05A recent poll showed that more people
would trust their hairdresser -- -
11:05 - 11:08(Laughter)
-
11:08 - 11:09or the man on the street
-
11:09 - 11:14than they would leaders of businesses,
the health service and even charities. -
11:14 - 11:18So we respect a teeth-whitening formula
discovered by a mom, -
11:18 - 11:21or we listen to an actress's view
on vaccination. -
11:21 - 11:24We like people who tell it like it is,
who go with their gut, -
11:24 - 11:26and we call them authentic.
-
11:27 - 11:30But gut feel can only get you so far.
-
11:31 - 11:35Gut feel would tell you never to give
water to a baby with diarrhea, -
11:35 - 11:38because it would just
flow out the other end. -
11:38 - 11:40Expertise tells you otherwise.
-
11:41 - 11:45You'd never trust your surgery
to the man on the street. -
11:45 - 11:48You'd want an expert
who spent years doing surgery -
11:48 - 11:50and knows the best techniques.
-
11:52 - 11:55But that should apply
to every major decision. -
11:55 - 12:00Politics, business, health advice
-
12:00 - 12:03require expertise, just like surgery.
-
12:04 - 12:08So then, why are experts so mistrusted?
-
12:09 - 12:12Well, one reason
is they're seen as out of touch. -
12:12 - 12:16A millionaire CEO couldn't possibly
speak for the man on the street. -
12:17 - 12:21But true expertise is found on evidence.
-
12:21 - 12:24And evidence stands up
for the man on the street -
12:24 - 12:26and against the elites.
-
12:26 - 12:29Because evidence forces you to prove it.
-
12:30 - 12:34Evidence prevents the elites
from imposing their own view -
12:34 - 12:35without proof.
-
12:37 - 12:39A second reason
why experts are not trusted -
12:39 - 12:42is that different experts
say different things. -
12:42 - 12:47For every expert who claimed that leaving
the EU would be bad for Britain, -
12:47 - 12:49another expert claimed it would be good.
-
12:49 - 12:53Half of these so-called experts
will be wrong. -
12:54 - 12:58And I have to admit that most papers
written by experts are wrong. -
12:59 - 13:02Or at best, make claims that
the evidence doesn't actually support. -
13:03 - 13:06So we can't just take
an expert's word for it. -
13:07 - 13:13In November 2016, a study
on executive pay hit national headlines. -
13:13 - 13:16Even though none of the newspapers
who covered the study -
13:16 - 13:18had even seen the study.
-
13:19 - 13:20It wasn't even out yet.
-
13:21 - 13:23They just took the author's word for it,
-
13:24 - 13:25just like with Belle.
-
13:26 - 13:29Nor does it mean that we can
just handpick any study -
13:29 - 13:31that happens to support our viewpoint --
-
13:31 - 13:33that would, again, be confirmation bias.
-
13:33 - 13:35Nor does it mean
that if seven studies show A -
13:35 - 13:37and three show B,
-
13:37 - 13:39that A must be true.
-
13:39 - 13:42What matters is the quality,
-
13:42 - 13:45and not the quantity of expertise.
-
13:46 - 13:48So we should do two things.
-
13:48 - 13:53First, we should critically examine
the credentials of the authors. -
13:54 - 13:58Just like you'd critically examine
the credentials of a potential surgeon. -
13:58 - 14:02Are they truly experts in the matter,
-
14:02 - 14:04or do they have a vested interest?
-
14:05 - 14:07Second, we should pay particular attention
-
14:07 - 14:11to papers published
in the top academic journals. -
14:12 - 14:16Now, academics are often accused
of being detached from the real world. -
14:17 - 14:20But this detachment gives you
years to spend on a study. -
14:20 - 14:22To really nail down a result,
-
14:22 - 14:24to rule out those rival theories,
-
14:24 - 14:27and to distinguish correlation
from causation. -
14:28 - 14:32And academic journals involve peer review,
-
14:32 - 14:34where a paper is rigorously scrutinized
-
14:34 - 14:35(Laughter)
-
14:35 - 14:37by the world's leading minds.
-
14:38 - 14:41The better the journal,
the higher the standard. -
14:41 - 14:46The most elite journals
reject 95 percent of papers. -
14:47 - 14:51Now, academic evidence is not everything.
-
14:51 - 14:54Real-world experience is critical, also.
-
14:54 - 14:58And peer review is not perfect,
mistakes are made. -
14:59 - 15:01But it's better to go
with something checked -
15:01 - 15:02than something unchecked.
-
15:03 - 15:06If we latch onto a study
because we like the findings, -
15:06 - 15:10without considering who it's by
or whether it's even been vetted, -
15:10 - 15:13there is a massive chance
that that study is misleading. -
15:15 - 15:17And those of us who claim to be experts
-
15:17 - 15:21should recognize the limitations
of our analysis. -
15:21 - 15:26Very rarely is it possible to prove
or predict something with certainty, -
15:26 - 15:31yet it's so tempting to make
a sweeping, unqualified statement. -
15:31 - 15:35It's easier to turn into a headline
or to be tweeted in 140 characters. -
15:36 - 15:40But even evidence may not be proof.
-
15:40 - 15:45It may not be universal,
it may not apply in every setting. -
15:45 - 15:50So don't say, "Red wine
causes longer life," -
15:50 - 15:55when the evidence is only that red wine
is correlated with longer life. -
15:55 - 15:58And only then in people
who exercise as well. -
16:00 - 16:04Tip number three
is "pause before sharing anything." -
16:05 - 16:08The Hippocratic oath says,
"First, do no harm." -
16:09 - 16:12What we share is potentially contagious,
-
16:12 - 16:16so be very careful about what we spread.
-
16:17 - 16:20Our goal should not be
to get likes or retweets. -
16:20 - 16:24Otherwise, we only share the consensus;
we don't challenge anyone's thinking. -
16:24 - 16:27Otherwise, we only share what sounds good,
-
16:27 - 16:29regardless of whether it's evidence.
-
16:30 - 16:33Instead, we should ask the following:
-
16:34 - 16:36If it's a story, is it true?
-
16:36 - 16:39If it's true, is it backed up
by large-scale evidence? -
16:39 - 16:41If it is, who is it by,
what are their credentials? -
16:41 - 16:44Is it published,
how rigorous is the journal? -
16:45 - 16:47And ask yourself
the million-dollar question: -
16:48 - 16:52If the same study was written by the same
authors with the same credentials -
16:53 - 16:55but found the opposite results,
-
16:56 - 16:59would you still be willing
to believe it and to share it? -
17:01 - 17:04Treating any problem --
-
17:04 - 17:08a nation's economic problem
or an individual's health problem, -
17:08 - 17:09is difficult.
-
17:09 - 17:14So we must ensure that we have
the very best evidence to guide us. -
17:14 - 17:17Only if it's true can it be fact.
-
17:18 - 17:20Only if it's representative
can it be data. -
17:21 - 17:24Only if it's supportive
can it be evidence. -
17:24 - 17:29And only with evidence
can we move from a post-truth world -
17:30 - 17:31to a pro-truth world.
-
17:32 - 17:34Thank you very much.
-
17:34 - 17:35(Applause)
- Title:
- What to trust in a "post-truth" world
- Speaker:
- Alex Edmans
- Description:
-
Only if you are truly open to the possibility of being wrong can you ever learn, says researcher Alex Edmans. In an insightful talk, he explores how confirmation bias -- the tendency to only accept information that supports your personal beliefs -- can lead you astray on social media, in politics and beyond, and offers three practical tools for finding evidence you can actually trust. (Hint: appoint someone to be the devil's advocate in your life.)
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 17:47
Oliver Friedman edited English subtitles for What to trust in a "post-truth" world | ||
Oliver Friedman edited English subtitles for What to trust in a "post-truth" world | ||
Brian Greene approved English subtitles for What to trust in a "post-truth" world | ||
Brian Greene edited English subtitles for What to trust in a "post-truth" world | ||
Krystian Aparta accepted English subtitles for What to trust in a "post-truth" world | ||
Krystian Aparta edited English subtitles for What to trust in a "post-truth" world | ||
Ivana Korom edited English subtitles for What to trust in a "post-truth" world | ||
Ivana Korom edited English subtitles for What to trust in a "post-truth" world |