金融财务高级17:38evaluating evidence

What to trust in a "post-truth" world

「后真相」时代，什么才值得信

伦敦商学院教授亚历克斯·埃德蒙斯从一桩刷屏的抗癌谎言讲起，层层拆解确认偏误：故事不等于事实，事实不等于数据，数据也不等于证据——因为它同样可能符合相互竞争的解释。他给出三条可操作的建议：主动找反面观点、看专家资历与期刊质量、转发前先停一下。术语密度高、论证链条长，适合练习英文中的推理与引证表达。

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台词(152 句)

00:05

Belle Gibson was a happy young Australian. She lived in Perth, and she loved skateboarding.

贝尔·吉布森是个快乐的澳大利亚年轻人。她住在珀斯，喜欢玩滑板。

00:11

But in 2009, Belle learned that she had brain cancer and four months to live.

但在 2009 年，贝尔得知自己患了脑癌，只剩四个月可活。

00:16

Two months of chemo and radiotherapy had no effect.

两个月的化疗和放疗毫无效果。

00:20

But Belle was determined. She'd been a fighter her whole life. From age six, she had to cook for her brother, who had autism, and her mother, who had multiple sclerosis.

但贝尔很坚定。她这一生都在战斗。从六岁起，她就得给患自闭症的哥哥和患多发性硬化症的母亲做饭。

00:30

Her father was out of the picture. So Belle fought, with exercise, with meditation and by ditching meat for fruit and vegetables.

父亲一直缺位。于是贝尔开始抗争：靠运动、靠冥想，靠把肉换成水果和蔬菜。

00:38

And she made a complete recovery. Belle's story went viral.

而她彻底康复了。贝尔的故事迅速传开。

00:43

It was tweeted, blogged about, shared and reached millions of people. It showed the benefits of shunning traditional medicine for diet and exercise.

它被转推、被写进博客、被到处分享，触达了数百万人。它似乎证明了：抛开传统医学、改用饮食和运动是有好处的。

00:52

In August 2013, Belle launched a healthy eating app, The Whole Pantry, downloaded 200,000 times in the first month.

2013 年 8 月，贝尔推出了一款健康饮食应用「The Whole Pantry」，第一个月就被下载了 20 万次。

01:04

But Belle's story was a lie. Belle never had cancer.

但贝尔的故事是个谎言。贝尔从来没得过癌症。

01:11

People shared her story without ever checking if it was true.

人们转发她的故事时，从来没有核实过它是不是真的。

01:16

This is a classic example of confirmation bias. We accept a story uncritically if it confirms what we'd like to be true.

这是确认偏误的典型例子。只要一个故事印证了我们希望为真的东西，我们就会不加批判地接受它，

01:24

And we reject any story that contradicts it. How often do we see this in the stories that we share and we ignore?

而对任何与之矛盾的故事一概拒绝。在我们转发的故事、以及我们视而不见的故事里，这种情况有多常见？

01:33

In politics, in business, in health advice.

政治上、商业上、健康建议上，都是如此。

01:38

The Oxford Dictionary's word of 2016 was "post-truth."

牛津词典 2016 年的年度词汇是「后真相」。

01:43

And the recognition that we now live in a post-truth world has led to a much needed emphasis on checking the facts.

而「我们如今活在后真相世界」这一认识，让大家把重点放在了核查事实上——这确实很有必要。

01:50

But the punch line of my talk is that just checking the facts is not enough.

但我这场演讲的关键在于：仅仅核查事实还不够。

01:55

Even if Belle's story were true, it would be just as irrelevant.

就算贝尔的故事是真的，它同样说明不了任何问题。

02:01

Why? Well, let's look at one of the most fundamental techniques in statistics.

为什么？我们来看看统计学里最基础的一种方法。

02:06

It's called Bayesian inference. And the very simple version is this: We care about "does the data support the theory?"

它叫贝叶斯推断。最简单的版本是这样的：我们真正关心的是「数据是否支持这个理论」。

02:16

Does the data increase our belief that the theory is true?

这些数据能不能提高我们对该理论为真的信心？

02:21

But instead, we end up asking, "Is the data consistent with the theory?"

但结果我们问的却是：「数据与这个理论一致吗？」

02:26

But being consistent with the theory does not mean that the data supports the theory.

可与理论一致，并不意味着数据支持这个理论。

02:32

Why? Because of a crucial but forgotten third term -- the data could also be consistent with rival theories.

为什么？因为还有一项关键却被遗忘的因素——数据也可能与相互竞争的理论一致。

02:41

But due to confirmation bias, we never consider the rival theories, because we're so protective of our own pet theory.

但由于确认偏误，我们从来不去考虑那些竞争性理论，因为我们太护着自己心爱的那套说法了。

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?

现在用它来看贝尔的故事。我们关心的是：贝尔的故事支持「饮食能治愈癌症」这个理论吗？

02:58

But instead, we end up asking, "Is Belle's story consistent with diet curing cancer?"

但结果我们问的却是：「贝尔的故事与『饮食治愈癌症』一致吗？」

03:05

And the answer is yes. If diet did cure cancer, we'd see stories like Belle's.

答案是「一致」。如果饮食真能治愈癌症，我们会看到贝尔这样的故事。

03:12

But even if diet did not cure cancer, we'd still see stories like Belle's.

可即便饮食并不能治愈癌症，我们照样会看到贝尔这样的故事：

03:18

A single story in which a patient apparently self-cured just due to being misdiagnosed in the first place.

某个病人看起来是自愈了，实际上只是因为一开始就被误诊了。

03:27

Just like, even if smoking was bad for your health, you'd still see one smoker who lived until 100.

就好比，即使吸烟有害健康，你照样能找到一个活到 100 岁的烟民。

03:34

(Laughter) Just like, even if education was good for your income, you'd still see one multimillionaire who didn't go to university.

（笑声）就好比，即使教育有助于提高收入，你照样能找到一个没上过大学的千万富翁。

03:42

(Laughter) So the biggest problem with Belle's story is not that it was false.

（笑声）所以贝尔这个故事最大的问题，不在于它是假的，

03:51

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.

而在于它只是一个故事。也许还有成千上万个「只靠饮食却失败了」的故事，只是我们从来听不到。

04:01

We share the outlier cases because they are new, and therefore they are news.

我们转发的是那些异常个案，因为它们新鲜，所以才成了新闻。

04:08

We never share the ordinary cases. They're too ordinary, they're what normally happens.

我们从不转发普通个案。它们太普通了，就是日常会发生的事。

04:14

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.

而那才是我们忽略掉的、占 99% 的真相。就像在社会里，你不能只听那 1% 的异类，却无视那 99% 的普通人。

04:25

Because that's the second example of confirmation bias. We accept a fact as data.

因为这就是确认偏误的第二种表现：我们把一个事实当成了数据。

04:32

The biggest problem is not that we live in a post-truth world; it's that we live in a post-data world.

最大的问题不是我们活在一个后真相世界，而是我们活在一个「后数据」世界。

04:41

We prefer a single story to tons of data.

比起成堆的数据，我们更爱一个故事。

04:46

Now, stories are powerful, they're vivid, they bring it to life. They tell you to start every talk with a story.

故事当然很有力量，它生动、鲜活。人家都教你演讲要用故事开场。

04:51

I did. But a single story is meaningless and misleading unless it's backed up by large-scale data.

我也这么做了。但除非有大规模数据撑腰，单个故事既没有意义，也会误导人。

05:02

But even if we had large-scale data, that might still not be enough.

但即便我们有了大规模数据，可能还是不够。

05:07

Because it could still be consistent with rival theories. Let me explain.

因为它仍然可能与竞争性理论一致。我来解释一下。

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.

心理学家彼得·沃森有一项经典研究：给你三个一组的数字，请你想出生成它们的规则。

05:22

So if you're given two, four, six, what's the rule?

假如给你的是 2、4、6，规则是什么？

05:28

Well, most people would think, it's successive even numbers. How would you test it?

大多数人会想：是连续的偶数。你会怎么检验？

05:33

Well, you'd propose other sets of successive even numbers: 4, 6, 8 or 12, 14, 16.

你多半会再提出几组连续偶数：4、6、8，或者 12、14、16。

05:41

And Peter would say these sets also work.

而彼得会告诉你，这些组也符合。

05:44

But knowing that these sets also work, knowing that perhaps hundreds of sets of successive even numbers also work, tells you nothing.

但知道这些组也符合、甚至知道几百组连续偶数都符合，其实什么也说明不了。

05:54

Because this is still consistent with rival theories.

因为这仍然与竞争性理论一致。

05:58

Perhaps the rule is any three even numbers.

规则也许是「任意三个偶数」。

06:02

Or any three increasing numbers.

又或者是「任意三个递增的数」。

06:05

And that's the third example of confirmation bias: accepting data as evidence, even if it's consistent with rival theories.

这就是确认偏误的第三种表现：把数据当成证据，哪怕它同样符合竞争性理论。

06:16

Data is just a collection of facts. Evidence is data that supports one theory and rules out others.

数据只是一堆事实的集合。证据则是既支持某个理论、又排除掉其他理论的数据。

06:26

So the best way to support your theory is actually to try to disprove it, to play devil's advocate.

所以，支持自己理论的最好办法，其实是试着推翻它，主动唱反调。

06:32

So test something, like 4, 12, 26.

所以去试一组，比如 4、12、26。

06:38

If you got a yes to that, that would disprove your theory of successive even numbers.

如果得到的回答是「符合」，那就推翻了你「连续偶数」的理论。

06:44

Yet this test is powerful, because if you got a no, it would rule out "any three even numbers" and "any three increasing numbers."

但这个检验很有力，因为如果回答是「不符合」，它就排除了「任意三个偶数」和「任意三个递增的数」。

06:53

It would rule out the rival theories, but not rule out yours.

它排除掉了竞争性理论，却没有排除你自己的理论。

06:57

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.

但大多数人不敢去试 4、12、26，因为他们不想得到肯定的回答，不想证明自己心爱的理论是错的。

07:08

Confirmation bias is not only about failing to search for new data, but it's also about misinterpreting data once you receive it.

确认偏误不只是不去寻找新数据，也包括拿到数据之后误读它。

07:17

And this applies outside the lab to important, real-world problems.

而这在实验室之外、在重要的现实问题上同样成立。

07:21

Indeed, Thomas Edison famously said, "I have not failed, I have found 10,000 ways that won't work."

的确，托马斯·爱迪生有句名言：「我没有失败，我只是找到了一万种行不通的方法。」

07:31

Finding out that you're wrong is the only way to find out what's right.

发现自己错了，是找到正确答案的唯一途径。

07:38

Say you're a university admissions director and your theory is that only students with good grades from rich families do well.

假设你是大学招生主任，你的理论是：只有出身富裕家庭、成绩又好的学生才读得好。

07:45

So you only let in such students. And they do well.

于是你只招这样的学生。而他们确实读得好。

07:49

But that's also consistent with the rival theory. Perhaps all students with good grades do well, rich or poor.

但这同样符合竞争性理论：也许所有成绩好的学生都读得好，不分贫富。

07:57

But you never test that theory because you never let in poor students because you don't want to be proven wrong.

可你永远不会去检验那个理论，因为你从不招贫困学生，因为你不想被证明是错的。

08:06

So, what have we learned? A story is not fact, because it may not be true.

那么，我们学到了什么？故事不等于事实，因为它可能不是真的。

08:13

A fact is not data, it may not be representative if it's only one data point.

事实不等于数据，只有一个数据点的话，它可能并不具备代表性。

08:20

And data is not evidence -- it may not be supportive if it's consistent with rival theories.

而数据不等于证据——如果它同样符合竞争性理论，它就构不成支持。

08:27

So, what do you do?

那么，你该怎么办？

08:30

When you're at the inflection points of life, deciding on a strategy for your business,

当你站在人生的转折点上——为公司定一套战略、

08:37

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?

为孩子选一种教养方式、为自己定一套健康方案——你怎么确保手里握的不是故事，而是证据？

08:47

Let me give you three tips. The first is to actively seek other viewpoints.

我给你们三条建议。第一条是主动去找不同的观点。

08:54

Read and listen to people you flagrantly disagree with. Ninety percent of what they say may be wrong, in your view.

去读、去听那些你明明白白不认同的人。在你看来，他们说的话可能 90% 都是错的。

09:02

But what if 10 percent is right?

但万一有 10% 是对的呢？

09:05

As Aristotle said, "The mark of an educated man is the ability to entertain a thought without necessarily accepting it."

正如亚里士多德所说：「受过教育的标志，是能容纳一种想法而未必接受它。」

09:16

Surround yourself with people who challenge you, and create a culture that actively encourages dissent.

让身边围着敢于挑战你的人，并营造一种主动鼓励异见的文化。

09:22

Some banks suffered from groupthink, where staff were too afraid to challenge management's lending decisions, contributing to the financial crisis.

有些银行栽在了群体思维上：员工不敢质疑管理层的放贷决定，最终助推了那场金融危机。

09:32

In a meeting, appoint someone to be devil's advocate against your pet idea.

开会时，指定一个人专门给你心爱的点子唱反调。

09:39

And don't just hear another viewpoint -- listen to it, as well.

而且不要只是「听见」另一种观点——还要真正听进去。

09:44

As psychologist Stephen Covey said, "Listen with the intent to understand, not the intent to reply."

正如心理学家史蒂芬·柯维所说：「带着理解的意图去听，而不是带着回应的意图去听。」

09:53

A dissenting viewpoint is something to learn from not to argue against.

相左的观点是用来学习的，不是用来反驳的。

09:59

Which takes us to the other forgotten terms in Bayesian inference.

这就把我们带回贝叶斯推断中另一个被遗忘的部分。

10:03

Because data allows you to learn, but learning is only relative to a starting point.

因为数据能让你学到东西，但学习永远是相对于某个起点而言的。

10:09

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.

如果你一开始就百分之百确信自己心爱的理论必然成立，那不管你看到什么数据，你的看法都不会变。

10:20

Only if you are truly open to the possibility of being wrong can you ever learn.

只有当你真心接受自己可能出错，你才谈得上学习。

10:27

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.

正如列夫·托尔斯泰所写：「最难的道理，也能向最迟钝的人解释清楚——只要他心里还没有形成任何成见。

10:37

But the simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already."

但最简单的道理，也无法向最聪明的人讲明白——只要他坚信自己早就懂了。」

10:48

Tip number two is "listen to experts."

第二条建议是「听专家的」。

10:52

Now, that's perhaps the most unpopular advice that I could give you.

这大概是我能给你们的、最不受欢迎的建议了。

10:56

(Laughter) British politician Michael Gove famously said that people in this country have had enough of experts.

（笑声）英国政治家迈克尔·戈夫有句名言：这个国家的人已经受够专家了。

11:05

A recent poll showed that more people would trust their hairdresser -- (Laughter) or the man on the street than they would leaders of businesses,

最近一项民调显示，比起企业领袖、

11:15

the health service and even charities.

医疗机构乃至慈善组织的负责人，更多人愿意相信自己的理发师——（笑声）或者街上随便一个路人。

11:17

So we respect a teeth-whitening formula discovered by a mom, or we listen to an actress's view on vaccination.

所以我们推崇某位妈妈发明的牙齿美白配方，或者听某位女演员谈疫苗接种。

11:24

We like people who tell it like it is, who go with their gut, and we call them authentic.

我们喜欢那种有话直说、凭直觉行事的人，还夸他们「真实」。

11:30

But gut feel can only get you so far. Gut feel would tell you never to give water to a baby with diarrhea, because it would just flow out the other end.

但直觉能带你走的路很有限。直觉会告诉你：千万别给腹泻的婴儿喝水，因为水会从另一头流出去。

11:41

Expertise tells you otherwise. You'd never trust your surgery to the man on the street.

专业知识告诉你的却恰恰相反。你绝不会把自己的手术交给街上随便一个人。

11:48

You'd want an expert who spent years doing surgery and knows the best techniques.

你会想找一个做了多年手术、掌握最佳术式的专家。

11:55

But that should apply to every major decision.

但这一点本该适用于每一个重大决定。

11:58

Politics, business, health advice require expertise, just like surgery.

政治、商业、健康建议都需要专业能力，和做手术一样。

12:07

So then, why are experts so mistrusted?

那么，专家为什么这么不受信任？

12:12

Well, one reason is they're seen as out of touch. A millionaire CEO couldn't possibly speak for the man on the street.

一个原因是他们被认为脱离现实。一位身家百万的 CEO，不可能替街上的普通人说话。

12:20

But true expertise is found on evidence. And evidence stands up for the man on the street and against the elites.

但真正的专业建立在证据之上。而证据恰恰是站在普通人一边、对抗精英的。

12:29

Because evidence forces you to prove it. Evidence prevents the elites from imposing their own view without proof.

因为证据逼着你拿出实证。证据不允许精英不加论证就把自己的观点强加于人。

12:40

A second reason why experts are not trusted is that different experts say different things.

专家不受信任的第二个原因是：不同的专家说法不一。

12:45

For every expert who claimed that leaving the EU would be bad for Britain, another expert claimed it would be good.

有一个专家说脱欧对英国不利，就会有另一个专家说脱欧对英国有利。

12:52

Half of these so-called experts will be wrong.

这些所谓的专家里，有一半必然是错的。

12:57

And I have to admit that most papers written by experts are wrong.

而我不得不承认，专家写的论文里，大多数都是错的。

13:02

Or at best, make claims that the evidence doesn't actually support.

或者往好里说，它们提出的结论，其实并没有证据支撑。

13:06

So we can't just take an expert's word for it. In November 2016, a study on executive pay hit national headlines.

所以我们不能光听专家一面之词。2016 年 11 月，一项关于高管薪酬的研究登上了全国头条。

13:16

Even though none of the newspapers who covered the study had even seen the study.

尽管报道这项研究的报纸，没有一家真正看过这项研究。

13:22

It wasn't even out yet. They just took the author's word for it, just like with Belle.

它当时甚至还没发表。它们只是把作者的话当真了——就跟贝尔那件事一样。

13:29

Nor does it mean that we can just handpick any study that happens to support our viewpoint -- that would, again, be confirmation bias.

这也不意味着我们可以随手挑一个刚好支持自己观点的研究——那又是确认偏误。

13:36

Nor does it mean that if seven studies show A and three show B, that A must be true.

更不意味着七项研究支持 A、三项支持 B，A 就一定为真。

13:42

What matters is the quality, and not the quantity of expertise.

重要的是专业性的质量，而不是数量。

13:49

So we should do two things. First, we should critically examine the credentials of the authors.

所以我们该做两件事。第一，我们应该批判性地审查作者的资历，

13:57

Just like you'd critically examine the credentials of a potential surgeon.

就像你会批判性地审查一位待选外科医生的资历一样。

14:01

Are they truly experts in the matter, or do they have a vested interest?

他们在这件事上真的是专家吗？还是说他们有既得利益？

14:08

Second, we should pay particular attention to papers published in the top academic journals.

第二，我们应该格外留意发表在顶级学术期刊上的论文。

14:15

Now, academics are often accused of being detached from the real world.

如今，学者常被指责脱离现实世界。

14:20

But this detachment gives you years to spend on a study. To really nail down a result, to rule out those rival theories, and to distinguish correlation from causation.

但正是这种「脱离」，让他们能拿出好几年来做一项研究：把一个结果真正钉死，排除掉那些竞争性理论，并把相关和因果区分开。

14:31

And academic journals involve peer review, where a paper is rigorously scrutinized (Laughter) by the world's leading minds.

而且学术期刊有同行评议，论文会被世界上最顶尖的头脑严格审视。（笑声）

14:41

The better the journal, the higher the standard. The most elite journals reject 95 percent of papers.

期刊越好，标准越高。最顶尖的期刊会拒掉 95% 的投稿。

14:50

Now, academic evidence is not everything. Real-world experience is critical, also.

当然，学术证据不是全部。现实世界的经验同样关键。

14:57

And peer review is not perfect, mistakes are made.

同行评议也并不完美，也会出错。

15:02

But it's better to go with something checked than something unchecked.

但被检验过的东西，总好过完全没被检验过的。

15:06

If we latch onto a study because we like the findings, without considering who it's by or whether it's even been vetted,

如果我们仅仅因为喜欢某项研究的结论就抓住不放，不管它出自谁手、有没有经过审核，

15:14

there is a massive chance that that study is misleading.

那这项研究极有可能是在误导我们。

15:18

And those of us who claim to be experts should recognize the limitations of our analysis.

而我们这些自称专家的人，也该认清自己分析能力的边界。

15:24

Very rarely is it possible to prove or predict something with certainty, yet it's so tempting to make a sweeping, unqualified statement.

能够确凿地证明或预测某件事的情况少之又少，可发表一句笼统而不加限定的断言，实在太诱人了。

15:34

It's easier to turn into a headline or to be tweeted in 140 characters.

那样更容易做成标题，或者用 140 个字发出去。

15:39

But even evidence may not be proof.

但即便是证据，也未必构成定论。

15:43

It may not be universal, it may not apply in every setting.

它可能不具普适性，未必在每一种情境下都成立。

15:48

So don't say, "Red wine causes longer life," when the evidence is only that red wine is correlated with longer life.

所以别说「红酒能延年益寿」——如果证据只表明红酒与长寿相关，

15:58

And only then in people who exercise as well.

而且还只在那些同时坚持运动的人身上成立。

16:03

Tip number three is "pause before sharing anything."

第三条建议是「分享之前先停一下」。

16:08

The Hippocratic oath says, "First, do no harm."

希波克拉底誓言说：「首先，不要造成伤害。」

16:12

What we share is potentially contagious, so be very careful about what we spread.

我们分享的东西可能会像传染病一样扩散，所以对自己传播的内容一定要非常小心。

16:20

Our goal should not be to get likes or retweets. Otherwise, we only share the consensus; we don't challenge anyone's thinking.

我们的目标不该是收获点赞或转发。否则我们只会分享共识，不会挑战任何人的思考。

16:27

Otherwise, we only share what sounds good, regardless of whether it's evidence.

否则我们只会分享听着顺耳的东西，而不管它算不算证据。

16:33

Instead, we should ask the following: If it's a story, is it true?

相反，我们应该这样问：如果这是个故事，它是真的吗？

16:39

If it's true, is it backed up by large-scale evidence? If it is, who is it by, what are their credentials?

如果它是真的，有大规模证据支撑吗？如果有，是谁做的，他们有什么资历？

16:44

Is it published, how rigorous is the journal?

它发表了吗？期刊有多严格？

16:48

And ask yourself the million-dollar question: If the same study was written by the same authors with the same credentials but found the opposite results,

再问自己一个价值百万的问题：如果同一项研究，由同样资历的同一批作者来做，却得出了相反的结论，

17:00

would you still be willing to believe it and to share it?

你还愿意相信它、还愿意分享它吗？

17:04

Treating any problem -- a nation's economic problem or an individual's health problem, is difficult.

处理任何问题——无论是一个国家的经济问题，还是一个人的健康问题——都很难。

17:12

So we must ensure that we have the very best evidence to guide us.

所以我们必须确保手里有最好的证据来指引我们。

17:17

Only if it's true can it be fact. Only if it's representative can it be data.

只有为真，它才算事实；只有具备代表性，它才算数据；

17:24

Only if it's supportive can it be evidence. And only with evidence can we move from a post-truth world to a pro-truth world.

只有构成支持，它才算证据。而只有靠证据，我们才能从后真相世界走向亲真相的世界。

17:35

Thank you very much. (Applause)

非常感谢。（掌声）