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Belle Gibson was a happy young Australian. She lived in Perth, and she loved skateboarding.
贝尔·吉布森是个年轻快乐的澳大利亚人。她住在佩斯，爱好滑板。
But in 2009, Belle learned that she had brain cancer and four months to live.
但是在2009年，贝尔发现自己得了脑癌，只剩下4个月的生命。
Two months of chemo and radiotherapy had no effect.
做了两个月的化疗和放射治疗也还是没有起色。
But Belle was determined. She'd been a fighter her whole life.
但贝尔决定要反抗命运。她从生下来就是个斗士。
From age six, she had to cook for her brother, who had autism, and her mother, who had multiple sclerosis.
她六岁的时候就开始为自己患有孤独症的弟弟和患有多发性硬化症的母亲做饭。
Her father was out of the picture.
而她的父亲对此完全置身事外。
So Belle fought, with exercise, with meditation and by ditching meat for fruit and vegetables.
于是贝尔做了反抗，坚持锻炼，冥想，戒荤食素。
And she made a complete recovery.
结果她痊愈了。
Belle's story went viral. It was tweeted, blogged about, shared and reached millions of people.
贝尔的故事在推特和博客上疯传，为数百万人所熟知。
It showed the benefits of shunning traditional medicine for diet and exercise.
她的故事表明了放弃传统药物、注意饮食、锻炼的好处。
In August 2013, Belle launched a healthy eating app, The Whole Pantry, downloaded 200,000 times in the first month.
到2013年8月，贝尔发布了一个健康饮食应用，全食，第一个月下载量就达到了20万次。
But Belle's story was a lie. Belle never had cancer.
但贝尔的故事其实是个谎言。她从来没有得过癌症。
People shared her story without ever checking if it was true.
人们分享她的故事的时候并没有去验证故事的真假。
This is a classic example of confirmation bias.
这就是确认偏误的一个经典案例。
We accept a story uncritically if it confirms what we'd like to be true.
如果有一件事我们希望它是真的，我们就会毫不怀疑的去接受这件事。
And we reject any story that contradicts it.
并且我们拒绝接受任何反驳的观点。
How often do we see this in the stories that we share and we ignore? In politics, in business, in health advice.
这种情况在我们分享和忽视的故事里出现的有多频繁呢？它出现在政治、商业领域，还有关于健康的建议里。
The Oxford Dictionary's word of 2016 was "post-truth."
牛津词典的2016年年度词汇是后真相。
And the recognition that we now live in a post-truth world has led to a much needed emphasis on checking the facts.
当意识到我们生活在一个后真相时代，我们就更加需要强调核实信息真实与否的必要性。
But the punch line of my talk is that just checking the facts is not enough.
但我演讲的重点是，仅仅核实信息的真实性是不够的。
Even if Belle's story were true, it would be just as irrelevant. Why?
即使贝尔的故事是真的，它也是不具有相关性的。为什么这么说呢？
Well, let's look at one of the most fundamental techniques in statistics. It's called Bayesian inference.
我们来看统计学领域最基本的一个规则。它叫做贝叶斯推断。
And the very simple version is this: We care about "does the data support the theory?"
简单说来，观点就是：我们在意这些数据可以用来支持这一理论吗？
Does the data increase our belief that the theory is true?
以及这些数据可以增加这一理论的可信度吗？
But instead, we end up asking, "Is the data consistent with the theory?"
但我们没有问这些数据跟这一理论相吻合吗？
But being consistent with the theory does not mean that the data supports the theory.
而数据与理论相吻合也并不意味着这些数据可以用来支持这一理论。
Why? Because of a crucial but forgotten third term -- the data could also be consistent with rival theories.
为什么呢？因为还有至关重要却常被人忽视的第三条规则--这些数据也可能和与其相悖的理论相吻合。
But due to confirmation bias, we never consider the rival theories, because we're so protective of our own pet theory.
但由于证实性偏见的存在，我们从来不会把与之相悖的理论考虑在内，因为我们想保护我们的宠物理论。
Now, let's look at this for Belle's story.
现在我们用贝尔的例子来看。
Well, we care about: Does Belle's story support the theory that diet cures cancer?
我们在意的是：贝尔的故事是否支持正确的饮食方法可以治疗癌症这一理论？
But instead, we end up asking, "Is Belle's story consistent with diet curing cancer?"
却不问，贝尔的故事跟正确的饮食方法可以治疗癌症这一理论是否完全吻合？
And the answer is yes. If diet did cure cancer, we'd see stories like Belle's.
答案是，是的。如果正确的饮食方法可以治愈癌症，我们眼里就会看到贝尔这样的故事。
But even if diet did not cure cancer, we'd still see stories like Belle's.
但如果正确的饮食方法无法治疗癌症，我们还是会看见贝尔的故事。
A single story in which a patient apparently self-cured just due to being misdiagnosed in the first place.
这就是个因为最一开始医生误诊，而病患最终自愈的故事。
Just like, even if smoking was bad for your health, you'd still see one smoker who lived until 100.
就好比是，即使吸烟有害健康，你还是会看见可以活到100岁的烟民。
Just like, even if education was good for your income, you'd still see one multimillionaire who didn't go to university.
就好比是，即使接受教育有利于增加收入，你还是会看见根本不上大学的百万富翁。
So the biggest problem with Belle's story is not that it was false. It's that it's only one story.
所以贝尔的故事中最大的问题并不在于它是假的。而在于它只是一个故事。
There might be thousands of other stories where diet alone failed, but we never hear about them.
可能世界上还有成百上千的故事告诉你光靠饮食是不够的，但我们听不到这些故事。
We share the outlier cases because they are new, and therefore they are news.
我们分享这些从没听过的案例，是因为它们是新的，然后就变成了新闻。
We never share the ordinary cases. They're too ordinary, they're what normally happens.
我们从来不分享普通的案例。因为他们太普通太常见了。
And that's the true 99 percent that we ignore.
而这些恰好就是我们忽视的99%。
Just like in society, you can't just listen to the one percent, the outliers, and ignore the 99 percent, the ordinary.
就像在社会生活里，我们不能只听那1%的此前不为人知的东西，而忽略了那99%的人人皆知的道理。
Because that's the second example of confirmation bias. We accept a fact as data.
因为这就是证实性偏见的第二个例子。我们把发生的事当数据。
The biggest problem is not that we live in a post-truth world; it's that we live in a post-data world.
最大的问题不在于我们生活在一个后真相的世界，而在于我们生活在一个后数据世界。
We prefer a single story to tons of data. Now, stories are powerful, they're vivid, they bring it to life.
我们宁愿相信一个简单的故事，也不愿相信大量的数据。故事是有力的、生动的，它们足够写实。
They tell you to start every talk with a story. I did.
他们说演讲要用故事开头。我就用了。
But a single story is meaningless and misleading unless it's backed up by large-scale data.
但除非有大量数据支持，否则一个简单的故事就毫无意义且容易造成误解。
But even if we had large-scale data, that might still not be enough.
但即使我们有大量的数据，可能还是不够。
Because it could still be consistent with rival theories. Let me explain.
因为数据也有可能跟与其相悖的理论相吻合。请容我解释一下。
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.
彼得·沃森曾做过一个经典研究，他会给你一组三个数字，让你说出生成这三个数字的规则。
So if you're given two, four, six, what's the rule?
如果给出的数字是2，4，6，那么规则是什么？
Well, most people would think, it's successive even numbers. How would you test it?
大部分人会觉得是一组连续偶数。怎么证实呢？
Well, you'd propose other sets of successive even numbers: 4, 6, 8 or 12, 14, 16.
可能你会举出另一组连续偶数来：4，6，8或者12，14，16。
And Peter would say these sets also work.
彼得会说那这组也符合这个规则啊。
But knowing that these sets also work, knowing that perhaps hundreds of sets of successive even numbers also work, tells you nothing.
但是即使知道这三组数字符合这个规则，可能还有成百上千的数字符合这个规则，这也一点用都没有。
Because this is still consistent with rival theories.
因为这个规则也符合与之相悖的其他理论。
Perhaps the rule is any three even numbers. Or any three increasing numbers.
也有可能规则是任意三个偶数或者任意三个逐渐递增的数字。
And that's the third example of confirmation bias: accepting data as evidence, even if it's consistent with rival theories.
这就是证实性偏见的第三个例子：把数据等同于证据，即使它还适用于其它相反的理论。
Data is just a collection of facts. Evidence is data that supports one theory and rules out others.
数据只是一系列事实的集合。而证据是支持一条理论且反对其它任何理论的数据。
So the best way to support your theory is actually to try to disprove it, to play devil's advocate.
所以支持自己的理论最好的方式就是尝试着去反驳，去故意唱反调。
So test something, like 4, 12, 26.
然后去做测试，比如4，12，26。
If you got a yes to that, that would disprove your theory of successive even numbers.
如果你得出的结果是符合，这就证明你的连续偶数规则不成立。
Yet this test is powerful, because if you got a no, it would rule out "any three even numbers" and "any three increasing numbers."
这个测试是很有用的，因为如果你得出的结果是不符合，那就证明任意三个偶数和任意三个逐渐递增的数字这两个规则不成立。
It would rule out the rival theories, but not rule out yours.
这样就会剔除掉与你的理论相反的理论，保住你自己的理论。
But most people are too afraid of testing the 4, 12, 26,
但是大部分人很害怕做4，12，26这个测试，
because they don't want to get a yes and prove their pet theory to be wrong.
因为他们害怕结果是符合，从而证明他们的宠物理论是错的。
Confirmation bias is not only about failing to search for new data,
证实性偏见不仅没能找出新的数据，
but it's also about misinterpreting data once you receive it.
还跟错误解读你手中的数据有关。
And this applies outside the lab to important, real-world problems.
这还适用于实验室外的重要的、现实世界的问题。
Indeed, Thomas Edison famously said, "I have not failed, I have found 10,000 ways that won't work."
托马斯·爱迪生曾说过一句著名的话：“我没有失败，我只是找到了10000种行不通的方法。”
Finding out that you're wrong is the only way to find out what's right.
认识自己的错才是唯一找到对的方式。
Say you're a university admissions director and your theory is that only students with good grades from rich families do well.
如果你是一个大学招生主任，你认为只有来自富裕家庭的成绩优秀的孩子才会表现好。
So you only let in such students. And they do well.
于是你只录取符合条件的孩子。他们表现的很好。
But that's also consistent with the rival theory.
但这一事实其实跟它的相反理论也是吻合的。
Perhaps all students with good grades do well, rich or poor.
可能不管家庭贫穷还是富有，成绩好的学生表现都很好。
But you never test that theory because you never let in poor students because you don't want to be proven wrong.
但你从来不测试自己理论的真实性，因为你从来不招家庭贫困的孩子，因为你怕证明自己是错的。
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呢？故事不是事实，因为它不一定是真的。
A fact is not data, it may not be representative if it's only one data point.
事实不是数据，如果它只有一个数据点的话，它就不那么有代表性。
And data is not evidence -- it may not be supportive if it's consistent with rival theories.
数据不是证据--如果它跟相反理论也吻合，它就不能用具有支持性。
So, what do you do? When you're at the inflection points of life,
那么你该怎么办？如果你正站在人生的转折点，
deciding on a strategy for your business, a parenting technique for your child or a regimen for your health,
需要决定为你的事业采取策略，为你的孩子选取教育技巧，或者为你的健康选取养生之道，
how do you ensure that you don't have a story but you have evidence?
怎样保证你了解到的不是一个故事而是证据呢？
Let me give you three tips. The first is to actively seek other viewpoints.
我来给你三个小建议。首先积极寻找其他观点。
Read and listen to people you flagrantly disagree with.
去读和听那些你完全不同意的观点。
Ninety percent of what they say may be wrong, in your view. But what if 10 percent is right?
可能在你看来，他们说的90%都是错的，但如果还有10%是对的呢？
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：“一个人受过教育的标志就在于他有明辨是非的能力。”
Surround yourself with people who challenge you, and create a culture that actively encourages dissent.
让你的周围都是跟你意见不同的人，创造一个接受不同意见的环境。
Some banks suffered from groupthink,
有些银行陷入了团体迷思，
where staff were too afraid to challenge management's lending decisions, contributing to the financial crisis.
他们的员工不敢反驳经理的借贷意见，最后导致了金融危机。
In a meeting, appoint someone to be devil's advocate against your pet idea.
所以开会时，需要有一个跟你意见不一样的人来反驳你。
And don't just hear another viewpoint -- listen to it, as well.
不要把不同的意见当耳边风，要认真倾听。
As psychologist Stephen Covey said, "Listen with the intent to understand, not the intent to reply."
心理学家史蒂芬·科维说过：“听是为了理解，不是为了反驳。”
A dissenting viewpoint is something to learn from not to argue against.
不同的意见的存在是为了让你从中学习而不是去争论反驳。
Which takes us to the other forgotten terms in Bayesian inference.
这就涉及了贝叶斯推断中的另一个容易被忘记的观点。
Because data allows you to learn, but learning is only relative to a starting point.
因为数据可以让你从中学习，但只在一开始的时候有效。
If you started with complete certainty that your pet theory must be true,
如果你从一开始就坚信自己的理论是正确的，
then your view won't change -- regardless of what data you see.
那你就观点就不会变了--不管你看到了什么数据。
Only if you are truly open to the possibility of being wrong can you ever learn.
只有对犯错的可能性保持开放的心态，你才能学到东西。
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：“如果一个人没有形成任何成见，就算他再笨，他也能够理解最困难的问题。
But the simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already."
但是如果一个人坚信那些摆在他面前的问题他早已了然于胸，即使这个人再聪明，多简单的道理他都不会明白。”
Tip number two is "listen to experts." Now, that's perhaps the most unpopular advice that I could give you.
第二个建议是“听专家的话。”这可能是我给出的最不受欢迎的建议。
British politician Michael Gove famously said that people in this country have had enough of experts.
英国政治家迈克尔·戈夫说过一句著名的话，即这个国家已经有足够多的专家了。
A recent poll showed that more people would trust their hairdresser
而最近的民意调查显示，他们更愿意相信自己的理发师，
or the man on the street than they would leaders of businesses, the health service and even charities.
或者甚至是街上的行人，而不是商业领袖、健康顾问甚至是慈善机构。
So we respect a teeth-whitening formula discovered by a mom, or we listen to an actress's view on vaccination.
于是我们就会信服一位妈妈的牙齿美白配方，或一位演员对疫苗的看法。
We like people who tell it like it is, who go with their gut, and we call them authentic.
我们喜欢实话实说的人，跟着直觉走的人，我们把这个叫做真实。
But gut feel can only get you so far.
但直觉也只能帮你到这里了。
Gut feel would tell you never to give water to a baby with diarrhea, because it would just flow out the other end.
直觉会告诉你不要给腹泻的孩子喂水，因为很快就会被排泄掉。
Expertise tells you otherwise. You'd never trust your surgery to the man on the street.
而专家告诉你不同的东西。但你永远都不会认为路上的行人比自己的手术医生更可信。
You'd want an expert who spent years doing surgery and knows the best techniques.
你会希望专家花多年时间做手术，希望他们掌握最好的技法。
But that should apply to every major decision.
但这种想法也应该应用到其他重要的决策中去。
Politics, business, health advice require expertise, just like surgery.
比如政治、商业和健康咨询中，就像做手术一样。
So then, why are experts so mistrusted? Well, one reason is they're seen as out of touch.
那么为什么专家这么没有可信度呢？其中一个原因就是他们离我们太远了。
A millionaire CEO couldn't possibly speak for the man on the street.
一个坐拥百万家产的CEO不可能为街上的行人发声。
But true expertise is found on evidence.
但真正的专业知识都是源自证据。
And evidence stands up for the man on the street and against the elites. Because evidence forces you to prove it.
证据会支持街上的行人，会反对精英。因为证据迫使你去证明。
Evidence prevents the elites from imposing their own view without proof.
证据不会让精英毫无根据的将自己的观点强加到别人身上。
A second reason why experts are not trusted is that different experts say different things.
第二个专家不被信任的原因是，不同的专家有不同的观点。
For every expert who claimed that leaving the EU would be bad for Britain, another expert claimed it would be good.
有些专家宣称脱欧对英国来说是坏事，有些则说是好事。
Half of these so-called experts will be wrong.
这其中有一半的所谓的专家是错的。
And I have to admit that most papers written by experts are wrong.
不得不说，大部分专家写的东西都是错的。
Or at best, make claims that the evidence doesn't actually support.
或者充其量，他们的证据并不足以支持他们的观点。
So we can't just take an expert's word for it.
所以我们不能直接拿专家的意见当事实。
In November 2016, a study on executive pay hit national headlines.
2016年11月，一份高管薪酬的研究上了全国的新闻头条。
Even though none of the newspapers who covered the study had even seen the study. It wasn't even out yet.
即使报道这篇研究内容的报社根本没见过这个研究。研究结果甚至还未发表。
They just took the author's word for it, just like with Belle.
他们只是相信了作者的话，就跟贝尔的案例一样。
Nor does it mean that we can just handpick any study that happens to support our viewpoint
我们不能随便挑一个发生过的事作为证据支持我们的论点，
that would, again, be confirmation bias.
因为这就会再次成为证实性偏见。
Nor does it mean that if seven studies show A and three show B, that A must be true.
如果七项研究得出结果A，三项得出结果B，并不意味着A是对的。
What matters is the quality, and not the quantity of expertise.
重要的是研究的质量，而不是数量。
So we should do two things. First, we should critically examine the credentials of the authors.
所以我们有两件事要做。首先，严格审核作者的资格证书。
Just like you'd critically examine the credentials of a potential surgeon.
就像你会严格审核一个潜在外科医生的证书一样。
Are they truly experts in the matter, or do they have a vested interest?
他们在这一问题上到底是专家，还是既得利益者？
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布在顶级学术刊物上的论文。
Now, academics are often accused of being detached from the real world.
现在经常有人批评学者脱离实际。
But this detachment gives you years to spend on a study.
而这种脱离让他们要花费几年的时间做研究。
To really nail down a result, to rule out those rival theories, and to distinguish correlation from causation.
要确实研究出结果，要排除错误选项，把相关性和因果关系区分开来。
And academic journals involve peer review, where a paper is rigorously scrutinized by the world's leading minds.
学术期刊发布涉及到同行的审核，一篇论文会被世界上同行业最优秀的人严格审查。
The better the journal, the higher the standard. The most elite journals reject 95 percent of papers.
期刊质量越高，标准也就越高。最优秀的期刊会拒绝掉95%的投稿。
Now, academic evidence is not everything. Real-world experience is critical, also.
学术上的证据并不是全部。现实生活中的经验也至关重要。
And peer review is not perfect, mistakes are made.
同行的审核并不完美，还是可能犯错。
But it's better to go with something checked than something unchecked.
但是经过核实的东西总比没经过的要好。
If we latch onto a study because we like the findings, without considering who it's by or whether it's even been vetted,
如果我们只因为喜欢一个研究结果就不考虑它的作者或检查它是否被审核过，
there is a massive chance that that study is misleading.
有很大几率这个研究会误导别人。
And those of us who claim to be experts should recognize the limitations of our analysis.
我们这些自称为专家的人应该意识到我们分析的局限性。
Very rarely is it possible to prove or predict something with certainty,
完全证明或确认一件事是对的，这种情况很少见，
yet it's so tempting to make a sweeping, unqualified statement.
而轻描淡写的做出一个不合格的陈述却很简单。
It's easier to turn into a headline or to be tweeted in 140 characters.
上头条和发一个140字的推文更简单。
But even evidence may not be proof. It may not be universal, it may not apply in every setting.
但是有的证据都不那么可信。它可能不是那么广泛的适用，它也许并不是在每个环节都起到相同的效果。
So don't say, "Red wine causes longer life," when the evidence is only that red wine is correlated with longer life.
所以当证据只表明喝红酒与长寿有一定联系的时候，别说“喝红酒可以延年益寿”。
And only then in people who exercise as well.
并且是只有当喝红酒的人也锻炼时候。
Tip number three is "pause before sharing anything." The Hippocratic oath says, "First, do no harm."
第三个建议是，跟别人分享什么东西之前请三思。《希波克拉底誓言》说“首要原则，不要造成伤害。”
What we share is potentially contagious, so be very careful about what we spread.
我们分享的东西有潜在的传染性，所以在分享前请一定要非常小心。
Our goal should not be to get likes or retweets.
我们的目的不是收到赞或者转发。
Otherwise, we only share the consensus; we don't challenge anyone's thinking.
否则，我们就应该只分享我们达成共识的东西，我们不挑战任何人的想法。
Otherwise, we only share what sounds good, regardless of whether it's evidence.
否则，我们就只分享那些听起来好的，不管是否有证据证实的东西。
Instead, we should ask the following: If it's a story, is it true?
相反，我们应该问如下问题：如果是个故事，那它是真实的吗？
If it's true, is it backed up by large-scale evidence?
如果是真实的，它有大规模的证据支持吗？
If it is, who is it by, what are their credentials? Is it published, how rigorous is the journal?
如果有，证据来自谁，他有资格证书吗？它出版了吗，出版刊物是否严谨？
And ask yourself the million-dollar question:
还要问一下你自己这个最有价值的问题：
If the same study was written by the same authors with the same credentials but found the opposite results,
如果同样是这个拥有同样证书的作者，做了同样的研究却发现了与之相反的结果，
would you still be willing to believe it and to share it?
你还愿意相信它并分享吗？
Treating any problem -- a nation's economic problem or an individual's health problem, is difficult.
处理任何问题--无论是国家经济问题还是个人的健康问题，都是很难的。
So we must ensure that we have the very best evidence to guide us.
所以我们必须保证我们有最好的证据来引导我们。
Only if it's true can it be fact. Only if it's representative can it be data.
只有正确的才能成为事实。只有有代表性的才能作为数据。
Only if it's supportive can it be evidence.
只有具备支持性的的才能成为证据。
And only with evidence can we move from a post-truth world to a pro-truth world. Thank you very much.
只有有证据，我们才能从后真相时代进入前真相时代。非常感谢。
