Belle Gibson was a happy young Australian. She lived in Perth, and she loved skateboarding. But in 2009, Belle learned that she had brain cancer and four months to live. Two months of chemo and radiotherapy had no effect. 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. 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.
贝尔·吉布森是一个 快乐的澳大利亚年轻人。 她住在珀斯, 她喜欢玩滑板。 但在2009年, 贝尔得知自己患有脑癌, 并且只有四个月可活。 两个月的化疗和放疗没有见效。 但是贝尔的意志很坚强, 贝尔一直都是一位斗士。 从6岁起, 她就得给患自闭症的哥哥, 还有患多发性硬化症的母亲做饭。 她的父亲一直缺位。 因此,贝尔用锻炼、冥想抗癌 同时,她也用蔬果代替肉食。 她完全康复了。
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.
贝尔的故事迅速走红。 她的故事在推特和博客上广为流传。 它展示了传统医学以外的 饮食和锻炼的意义。 2013年8月,贝尔发布了 一款健康饮食的应用软件, “健康厨房” 首月下载量达到20万次。
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 business, in health advice.
但贝尔的故事是个谎言。 贝尔从来没有过癌症。 人们分享她的故事时 从未去检验其真实性。 这是肯证偏差的典型例子。 我们会不加批判地接受一个故事, 当它证实了我们认为是真实的事时, 并且我们会拒绝 任何与之相悖的故事。 我们看到了多少这种情况? 在我们分享却忽略的故事中。 在政治上,在商业中, 在健康建议上。
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 irrelevant. Why?
牛津词典2016年的 年度词汇是“后真相”。 人们意识到 我们正处在后真相世界中, 所以如今我们非常强调核查事实。 但我演讲的重点是 仅仅去核查真相还不够。 即便贝尔的故事是真的, 它也是一个不相关的故事。 为什么?
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.
让我们看看统计学中的 一个基本原理。 贝叶斯推理。 它的核心观点就是, 我们关心:“数据是否支持这个理论?” 这个数据是否能够证实 这个理论为真 但相反,我们最终会问, “数据是否与理论一致?” 但与理论一致 不等于数据支持这个理论。 为什么? 因为有一个关键但被人遗忘的点—— 数据也可以和对立的理论一致。 但由于肯证偏差, 我们从不考虑对立的理论, 因为我们袒护 我们的宠物理论。
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 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.
现在,让我们看看贝尔的故事。 我们关心的是:贝尔的故事 支持饮食治愈癌症的理论吗? 但相反,我们最终问的是: “贝尔的故事 等同于饮食治愈癌症吗?” 答案是肯定的。 如果饮食可以治愈癌症, 我们会看到像贝尔这样的故事。 但即便饮食不能治疗癌症, 我们仍然会看到像贝尔这样的故事。 比如一个病人自我治愈 只是因为一开始被误诊。 或者,即使吸烟有害健康, 你仍然会看到一个烟民活到100岁。
(Laughter)
(笑声)
Just like, even if education was good for your income, you'd still see one multimillionaire who didn't go to university.
又或者,即使接受教育 有助于增加你的收入, 你仍会看到没上过大学的千万富翁。
(Laughter)
(笑声)
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.
所以贝尔故事最大的问题 不在于它是虚假的。 在于它只是一个故事。 也许有成千上万仅靠饮食失败的故事, 但我们从没听到这些故事。
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.
我们分享异常个案 只是因为它们是新奇的, 因此它们成了新闻。 我们从不分享普通案例。 它们太普通, 它们就是日常发生的事情。 这是我们忽略的99%的真相。 就像在社会中, 你不能只听1%的异常个案, 去忽略99%的普通事实。
Because that's the second example of confirmation bias. We accept a fact as data. The biggest 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 bring 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.
因为这是第二个肯证偏差的例子, 我们接受事实作为数据。 最大的问题不在于 我们生活在后真相世界; 在于我们生活在后数据世界。 比起大量的数据, 我们更喜欢简单的故事。 那些强大的,生动的,鲜活的故事。 他们告诉你 演讲要用故事开场。 我也是这样做的。 但一个简单的故事 是没有意义且误导人的, 除非它有大量的数据支持。 但即便我们有大量的数据, 这可能仍然不够。 因为它可能仍然与对立结论一致。 让我解释一下。
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. 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.
心理学家彼得·沃森的一项经典研究 给你一组三个数据 并让你思考产生这些数据的规律。 如果他们给了你三个数字: 2,4,6, 规律是什么? 很多人会认为,这是连续的偶数。 你会如何检验它? 你可以提出其他连续偶数的组合: 4,6,8或者12,14,16. 彼得说这些数组也行。 但知道这些数组也行, 知道数百组连续的偶数也可以, 这个结论形同虚设。 因为这仍然与对立理论一致。 也许规则可能是任意三个偶数。 或者任何三个不断增加的数字。
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.
这是第三个肯证偏差的例子: 接受数据作为证据, 即便它与对立结论一致。 数据只是事实的组合。 证据是支持一种理论 排除其他理论的数据。 所以支持你的理论最好的方法是 试图去反驳它, 做魔鬼的代言人(唱反调)。 所以检验一下, 比如4,12,26. 如果你的答案是肯定的, 那么就证明 你的连续偶数理论是不成立的。 这个检验很有力, 因为如果答案不是, 就可以排除“任何三个偶数” 和“任何三个不断增长的数字”。 它会排除对立理论, 但不排除你的理论。 但大部分人 不敢用4,12,26检验, 因为他们不想肯定, 也不想承认他们的宠物理论是错的。 肯证偏差不仅指 未能搜索到新的数据, 它也与你误解数据有关。
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.
这个理论也适用于 实验室外的现实世界。 的确,托马斯•爱迪生有句名言 我没有失败, 我成功地发现了1万种行不通的方法 发现你的错误 是通往成功的唯一道路。
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.
假设你是大学招生办主任, 你的理论是只有来自富裕家庭 成绩好的学生才表现好。 所以你只招这些学生, 他们都表现很好。 但这也跟对立理论一致。 可能所有成绩好的学生都表现好, 不论富裕或贫穷。 但你永远不会测试这个理论, 因为你不会招贫穷学生, 因为你不想被证明错误。
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?
那么,我们学到了什么? 一个故事不是事实, 因为它可能不是真的。 一个事实不是数据, 如果只是一个数据点, 它可能不具有代表性。 数据不是证据—— 如果它与对立理论一致, 就不具有支持性。 你会怎么做? 当你处在人生的转折点, 去选择生意的策略, 孩子的育儿技巧, 或者健康养生, 你如何确保你不是基于故事 而是你拥有证据?
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." Surround 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.
让我给你们三个提示。 首先是积极寻求其他观点。 阅读和倾听你公然不同意的人。 在你看来,他们说的90%都不对。 但如果还有10%是对的呢? 亚里士多德说过, “受过教育的标志是, 你可以不接受一种观点, 但你能容纳它。” 和挑战你的人在一起, 创造一种积极鼓励异见的环境。 一些银行受到群体思维的影响, 员工不敢挑战管理者的借贷决策, 引发了金融危机。 开会的时候, 指定某人充当魔鬼的代言人 挑战你的宠物理论。 不要只让这些观点从你的脑后飘过, 请认真倾听。
As psychologist Stephen Covey said, "Listen with the intent to understand, not the intent to reply." 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.
正如心理学家斯蒂芬·柯维所说, “抱着理解的态度倾听, 别只想着怎么回答。” 对立的观点是值得学习的, 而不是不假思索地反对。 这使我们想到 贝叶斯推断中被遗忘的部分 因为数据给你学习的空间, 但学习只是一个起点。 如果开始就完全确信 你的宠物理论必然成立, 那么你的观点不会改变—— 不管你看到的数据是什么。
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.
只有你真正接受犯错的可能性时, 你才能学习。 正如列夫·托尔斯泰所写, “最难的事情 也可以向最迟钝的人解释清楚, 只要他还没有 形成任何关于此问题的见解。 但最简单的事情 却无法向最聪明的人说清楚, 如果他确信他已经知道答案。“ 第二个提示是:“听专家的。” 这可能是我给你的 最不流行的建议了。
(Laughter)
(笑声)
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 --
英国政治家迈克尔·戈夫曾说过: 这个国家的人民已经受够专家了。 最近的调查显示 更多人相信他们的理发师——
(Laughter)
(笑声)
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 tell you never to give water to a baby with diarrhea, because it would just flow out the other end. Expertise tells you otherwise. You'd never trust your surgery to the man on the street. You'd want an expert who spent years doing surgery and knows the best techniques. But that should apply to every major decision. Politics, business, health advice require expertise, just like surgery.
或者街上的路人, 而非商界领袖、医疗服务机构、 甚至慈善机构的领导人。 所以我们敬仰一位母亲 发现的牙齿美白配方, 或者我们会听 女演员对疫苗接种的看法。 我们喜欢说实话、凭直觉做事的人, 我们觉得这叫真实。 但直觉只能让你走这么远。 直觉会告诉你永远不要 给腹泻的婴儿喝水, 因为它会从另外一端流出。 专家告诉你,事实并非如此。 你绝不会把你的手术交给街上的人。 你想要一个拥有多年手术经验 并且有最佳技巧的专家。 但这点应该应用到每个重要的决定中。 政治,商业,健康建议都需要专家, 就跟做手术一样。
So then, why are experts so mistrusted? Well, one reason is they're seen as out of touch. A millionaire CEO couldn't possibly speak for the man on the street. But true expertise is found on evidence. And evidence stands up for the man on the street and against the elites. Because evidence forces you to prove it. Evidence prevents the elites from imposing their own view without proof.
那么,为什么专家如此不被信任呢? 一个原因是他们脱离群众。 一个年薪百万的总裁 不可能为街头的人发声。 但真正的专业知识来自于证据。 证据支持街上的人, 反对精英。 因为证据迫使你证明它。 证据不允许精英们强加他们的观点 在没有证明的情况下。
A second reason why experts are not trusted is that different experts say different things. For every expert who claimed that leaving the EU would be bad for Britain, another expert claimed it would be good. Half of these so-called experts will be wrong. And I have to admit that most papers written by experts are wrong. Or at best, make claims that the evidence doesn't actually support. So we can't just take an expert's word for it.
第二个专家不被信任的理由是, 不同的专家观点不同。 只要有专家说 脱欧对英国而言弊端重重, 就会有另外一个专家说 脱欧对英国而言好处良多。 这些所谓的专家, 他们一半的观点都是错的。 我不得不承认,大多数专家 写的论文也都是错的。 换一种好听一点的说法, 做出证据并不支持的断言。 所以我们不能只相信专家。
In November 2016, a study on executive pay hit national headlines. Even though none of the newspapers who covered the study had even seen the study. It wasn't even out yet. They just took the author's word for it, just like with Belle. Nor does it mean that we can just handpick any study that happens to support our viewpoint -- that would, again, be confirmation bias. Nor does it mean that if seven studies show A and three show B, that A must be true. What matters is the quality, and not the quantity of expertise.
2016年11月,一个关于 高管薪酬的研究登上国家头条。 尽管报道这项研究的报社 没有一家看过这项研究。 这项研究甚至还没有发表。 他们只是把作者的话当真了, 就像贝尔的故事一样。 这不意味着我们可以随便挑选一个 刚好支持我们观点的研究—— 这也是肯证偏差。 也不意味着7项研究表明A, 三项研究表明B, A就必然是真的。 重点在于质量, 而不是专家的数量。
So we should do two things. First, we should critically examine the credentials of the authors. Just like you'd critically examine the credentials of a potential surgeon. Are they truly experts in the matter, or do they have a vested interest? Second, we should pay particular attention to papers published in the top academic journals. Now, academics are often accused of being detached from the real world. 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. And academic journals involve peer review, where a paper is rigorously scrutinized
所以我们应该做两件事。 首先,我们应该 严格审查作者的资历, 就像你会谨慎地审视一个 外科医生的资质一样。 他们真的是这个领域的专家吗, 或者他们有没有既得利益? 第二,我们应该特别注意 发布在顶级期刊上的论文。 如今, 学术界常常被指责与现实世界脱节。 但这种脱节 给了你充足的时间去研究。 去真正确定一个结果, 去排除那些对立的理论, 并且区分因果关系。 学术期刊涉及同行评议, 在这个环节,论文会被严格审查
(Laughter)
(笑声)
by the world's leading minds. The better the journal, the higher the standard. The most elite journals reject 95 percent of papers.
被学术界的尖端代表审查。 越好的期刊,标准越高。 最顶级期刊的论文拒绝率高达95%
Now, academic evidence is not everything. Real-world experience is critical, also. And peer review is not perfect, mistakes are made. But it's better to go with something checked than something unchecked. If we latch onto a study because we like the findings, without considering who it's by or whether it's even been vetted, there is a massive chance that that study is misleading. And those of us who claim to be experts should recognize the limitations of our analysis. Very rarely is it possible to prove or predict something with certainty, yet it's so tempting to make a sweeping, unqualified statement. It's easier to turn into a headline or to be tweeted in 140 characters. But even evidence may not be proof. It may not be universal, it may not apply in every setting. So don't say, "Red wine causes longer life," when the evidence is only that red wine is correlated with longer life. And only then in people who exercise as well.
如今,学术证据并不是一切。 现实世界的经验也很重要。 同行评议也不尽完美,常犯错误。 但有检查 总比没有检查好。 如果我们青睐一个研究 是因为我们喜欢这个发现, 而不考虑它是谁做的 或者它是否经过审查, 这个研究就很有可能误导人。 我们这些自称专家的人, 需要认识到我们分析能力的局限性。 确切地证明或预测某事 的可能性是很小的, 然而,发表一份全面、 不够格的声明十分诱人。 它们往往能成为头条 或者微博热点 即便证据并不充分详实。 它可能不是通用的, 可能不适用于任何条件。 所以不要说, “红酒能延长寿命,” 当证据只是在 红酒与长寿相关时, 并且样本局限在运动人群中。
Tip number three is "pause before sharing anything." The Hippocratic oath says, "First, do no harm." What we share is potentially contagious, so be very careful about what we spread. Our goal should not be to get likes or retweets. Otherwise, we only share the consensus; we don't challenge anyone's thinking. Otherwise, we only share what sounds good, regardless of whether it's evidence.
提示三: “分享任何事情前先三思。” 希波克拉底誓言(医者誓言)说, “首先,不要伤害。” 我们分享的东西 有可能会快速蔓延, 所以要谨慎地对待 我们散布的东西。 我们的目的不应该是为了 获得点赞或转发。 否则,我们只会分享共识; 我们不会挑战任何人的思考。 否则,我们只分享听起来好的, 无视其是否是证据。
Instead, we should ask the following: If it's a story, is it true? If it's true, is it backed up by large-scale evidence? If it is, who is it by, what are their credentials? Is it published, how rigorous is the journal? 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, would you still be willing to believe it and to share it?
反过来,我们应该问如下问题: 如果这是个故事,这是真的吗? 如果这是真的, 有大量的证据支持吗? 如果有,证据是谁提供的, 他们的凭证是什么? 它发表了吗? 这个期刊是否足够权威? 并且郑重地问自己, 如果同样的研究 是同等资质的同一作者写的, 但发现的是对立理论, 你仍然愿意相信和分享它吗?
Treating any problem -- a nation's economic problem or an individual's health problem, is difficult. So we must ensure that we have the very best evidence to guide us. Only if it's true can it be fact. Only if it's representative can it be data. 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.
处理任何问题—— 国家经济问题或者个人健康问题, 很难。 所以我们必须确保 有最佳证据指引我们。 只有它是真的,才能成为事实。 只有具有代表性,才能成为数据。 只有被支持,才能是证据。 只有是证据,我们才能从后真相世界 走向支持真相的世界。
Thank you very much.
谢谢。
(Applause)
(鼓掌)