The Feynman Learning Technique 费曼学习法


If you’re after a way to supercharge your learning and become smarter, the Feynman Technique might just be the best way to learn absolutely anything. Devised by a Nobel Prize-winning physicist, it leverages the power of teaching for better learning.
如果你想猛增你的学识,变得更聪明,‘Feynman Technique'可能是最好的的学习方法。这是一个物理诺贝尔奖获得者创立的,它利用教学的力量来更好的学习。

The Feynman Learning Technique is a simple way of approaching anything new you want to learn.
它是学习新东西的一种简单的方法。

Why use it? Because learning doesn’t happen from skimming through a book or remembering enough to pass a test. Information is learned when you can explain it and use it in a wide variety of situations. The Feynman Technique gets more mileage from the ideas you encounter instead of rendering anything new into isolated, useless factoids.
为什么要用它?因为学习不是翻一本书或记住一些东西通过一个考试,而是你学会了,能够向别人解释,能够在别的情况下使用。它让你学到的东西得到衍生扩展,而不是学习孤立的、无用的知识。

When you really learn something, you give yourself a tool to use for the rest of your life. The more you know, the fewer surprises you will encounter, because most new things will connect to something you already understand.
你学会就要去用,你学的越多,新的惊喜就越少,因为很多新的都将会和你已经理解的相关。

Ultimately, the point of learning is to understand the world. But most of us don’t bother to deliberately learn anything. We memorize what we need to as we move through school, then forget most of it. As we continue through life, we don’t extrapolate from our experiences to broaden the applicability of our knowledge. Consequently, life kicks us in the ass time and again.
归根结底,获得知识的目的是为了认识这个世界。我们大多不喜欢刻意的去学习。上学时我们记住需要记住的,然后忘掉大多数。生活中我们又不去从经历中拓展我们相关的知识。

To avoid the pain of being bewildered by the unexpected, the Feynman Technique helps you turn information into knowledge that you can access as easily as a shirt in your closet.
为了避免被意外的事折腾,它帮助你将信息转化成你随时可用的知识。


The Feynman Technique

“Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius—and a lot of courage—to move in the opposite direction.” —E.F. Schumacher


任何人都可以事情搞得很复杂、更大、更暴力。而你想把事情做好则需要点天才和勇气。

There are four steps to the Feynman Learning Technique, based on the method Richard Feynman originally used. We have adapted it slightly after reflecting on our own experiences using this process to learn. The steps are as follows:
在他的原来的方法基础上,我们根据使用的经验调整为四步:

  1. Pretend to teach a concept you want to learn about to a student in the sixth grade.
  2. Identify gaps in your explanation. Go back to the source material to better understand it.
  3. Organize and simplify.
  4. Transmit (optional).

1. 学习一个概念时,假装你在教一个6年纪的学生。
( 好,我解释清楚了,那么下面我还要做什么?)
2.找到你解释中的漏洞,回去再去理解。
(也就是在讲的时候,有些没说清楚,或者漏掉了)
3.将知识组织起来,让它变得简单的一段话。
(这是要提炼,加强和现有知识的连接)
4. 传播出去(可选)
(这个是知识再次提取出来,提取的次数越多,知识记得越牢,当然这个过程可能得到反馈,也许会修正一些知识)


怎么来做第一步

Take out a blank sheet of paper. At the top, write the subject you want to learn. Now write out everything you know about the subject as if you were teaching it to a child or a rubber duck sitting on your desk. You are not teaching to your smart adult friend, but rather a child who has just enough vocabulary and attention span to understand basic concepts and relationships.
拿一张空白纸,顶上写你想学习的题目。然后写出你知道的一切,假装教一个词汇有限的小孩,用最基本的概念。而不是教一个聪明的成年朋友。

Or, for a different angle on the Feynman Technique, you could place a rubber duck on your desk and try explaining the concept to it. Software engineers sometimes tackle debugging by explaining their code, line by line, to a rubber duck. The idea is that explaining something to a silly-looking inanimate object will force you to be as simple as possible.
你也可以教一个橡皮鸭,向它解释这个概念。软件工程师常常通过向一只橡皮鸭一行一行的解释代码来解决bug,向一个一无所知的玩偶解释需要你用最简单的语言。

It turns out that one of the ways we mask our lack of understanding is by using complicated vocabulary and jargon. The truth is, if you can’t define the words and terms you are using, you don’t really know what you’re talking about. If you look at a painting and describe it as “abstract” because that’s what you heard in art class, you aren’t displaying any comprehension of the painting. You’re just mimicking what you’ve heard. And you haven’t learned anything. You need to make sure your explanation isn’t above, say, a sixth-grade reading level by using easily accessible words and phrases.
我们用复杂的词汇和jargon来掩盖我们的无知,如果你不能定义这些词或术语,你就不能真正理解你自己在说什么。 比如你看一幅画,说它是抽象的,那么你就没有理解这幅画,只不过是你在模仿你听到的,你什么都没有学到。你需要能够用小学生能理解的语言解释这幅画。

When you write out an idea from start to finish in simple language that a child can understand, you force yourself to understand the concept at a deeper level and simplify relationships and connections between ideas. You can better explain the why behind your description of the what.
用简单到孩子都能听懂的语言解释了一个概念后,你会更深层次的理解,更好的解释为什么这样去解释。

Looking at that same painting again, you will be able to say that the painting doesn’t display buildings like the ones we look at every day. Instead it uses certain shapes and colors to depict a city landscape. You will be able to point out what these are. You will be able to engage in speculation about why the artist chose those shapes and those colors. You will be able to explain why artists sometimes do this, and you will be able to communicate what you think of the piece considering all of this. Chances are, after capturing a full explanation of the painting in the simplest possible terms that would be easily understood by a sixth-grader, you will have learned a lot about that painting and abstract art in general.
再看这幅画,你会说这幅画不想我们看的建筑,他用一些形状和颜色来描绘城市景观。猜测为什么要用这些形状和颜色,为什么要这么做,表达你的想法。这么做之后,你很可能学会了关于这幅画的抽象艺术知识。

Some of capturing what you would teach will be easy. These are the places where you have a clear understanding of the subject. But you will find many places where things are much foggier.
一些清晰东西解释起来比较容易点,你也可以清晰的理解。但是还有些地方会比较模糊。


第二步
Areas where you struggle in Step 1 are the points where you have some gaps in your understanding.
在第一步里面理解的困惑的点,就是你的盲区。

Identifying gaps in your knowledge—where you forget something important, aren’t able to explain it, or simply have trouble thinking of how variables interact—is a critical part of the learning process. Filling those gaps is when you really make the learning stick.
找到你的知识盲点--哪些你遗忘的重点和你解释不清楚的地方或者,或者想不清楚 变量之间的互相作用----这是个重要的学习过程。 填补这些盲区可以让你牢记你学到的东西

Now that you know where you have gaps in your understanding, go back to the source material. Augment it with other sources. Look up definitions. Keep going until you can explain everything you need to in basic terms.
知道自己知识盲区在哪后,回到原始的学习资料。用其它的资料加强理解它。找各种定义,知道你能够用普通的词语解释全部内容。

Only when you can explain your understanding without jargon and in simple terms can you demonstrate your understanding. Think about it this way. If you require complicated terminology to explain what you know, you have no flexibility. When someone asks you a question, you can only repeat what you’ve already said.
只有你不用专业术语解释,你才能用简单的语言就能表达你的理解。想象一下,如果你依赖复杂术语解释,那么你就不够灵活了,因为别人再问你时,你依然只能重复说过的话。

Simple terms can be rearranged and easily combined with other words to communicate your point. When you can say something in multiple ways using different words, you understand it really well.
简单的措辞容易重新组合去表达你的观点。当你可以用不同的单词多种方式表达时,你就已经很好的理解了。

Being able to explain something in a simple, accessible way shows you’ve done the work required to learn. Skipping it leads to the illusion of knowledge—an illusion that can be quickly shattered when challenged.
有能力用简单的、容易理解的方式解释说明你掌握了你要学的东西。跳过这个步骤的话,会让你幻想你已经掌握了---但是当你遇到挑战,你以为你会的幻想会很快被打碎。

Identifying the boundaries of your understanding is also a way of defining your circle of competence. When you know what you know (and are honest about what you don’t know), you limit the mistakes you’re liable to make and increase your chance of success when applying knowledge.
搞清楚到你理解的边界是确定你的认知范围的一个办法。当你知道你知道(知道哪些不知道),你就减少了因为认为自己知道实际不知道而犯的错,也会因你掌握的知识而增加成功的机会。

第三步 

Now you have a set of hand-crafted notes containing a simple explanation. Organize them into a narrative that you can tell from beginning to end. Read it out loud. If the explanation sounds confusing at any point, go back to Step 2. Keep iterating until you have a story that you can tell to anyone who will listen.
现在你有了很多手稿包括这简单的解释。把他们从头到尾的组织起来,变成可以向他人完整描述的一段话。大声读出来。如果这个解释某些点听起来有些迷糊,返回第二步,如此反复,一直到你有个可以讲给别人听的故事。

If you follow this approach over and over, you will end up with a binder full of pages on different subjects. If you take some time twice a year to go through this binder, you will find just how much you retain.
如果你持续的安装这个方法,你会得到一本写满了不同主题的binder. 如果你每年看2次这个binder,你将会发现你还记得不少。


Step 4: Transmit (optional)

This part is optional, but it’s the logical result of everything you’ve just done. If you really want to be sure of your understanding, run it past someone (ideally someone who knows little of the subject). The ultimate test of your knowledge is your capacity to convey it to another. You can read out directly what you’ve written. You can present the material like a lecture. You can ask your friends for a few minutes of their time while you’re buying them dinner. You can volunteer as a guest speaker in your child’s classroom or your parents’ retirement residence. All that really matters is that you attempt to transmit the material to at least one person who isn’t that familiar with it.
这是你完成的所有的事情后需要做的,如果你想确认你你的理解,找了解这个很少的人。终极检测你的知识的方式就是你有能力告诉别人。你可以直接读你写的。你可以表现的向在演讲。你可以请你朋友吃饭的时候问问他们。你可以作为自愿者去你的小孩的学校或者父母住所。这些都是你可以传播你对这个主题的理解的途径,你至少要找一位不熟悉这个问题的人来讲。

The questions you get and the feedback you receive are invaluable for further developing your understanding. Hearing what your audience is curious about will likely pique your own curiosity and set you on a path for further learning. After all, it’s only when you begin to learn a few things really well do you appreciate how much there is to know.
获得问题的反馈,对你未来的加强你的理解是非常宝贵的。了解你的听众好奇会激起你的好奇心,毕竟只有当你开始学到一些新东西,你才会意识到还有很多东西都要搞清楚。

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The Feynman Technique is not only a wonderful recipe for learning but also a window into a different way of thinking that allows you to tear ideas apart and reconstruct them from the ground up.
费曼学习法不仅仅是获取知识的好办法,也是进入一种不同的思维方式的窗口,你可以通过它把思想打散并从头组合。

When you’re having a conversation with someone and they start using words or relationships that you don’t understand, ask them to explain it to you like you’re twelve.
当你在交谈中听到你不知道的单词和关系时,把自己当做是12岁的孩子,向他们询问。

Not only will you supercharge your own learning, but you’ll also supercharge theirs.
这样的话你不仅加强了你的记忆,也加强了他们的理解。

Feynman’s approach intuitively believes that intelligence is a process of growth, which dovetails nicely with the work of Carol Dweck, who describes the difference between a fixed and growth mindset.
费曼的方法直观的认为智力是个成长的过程,与卡罗尔德维克的工作吻合,他描述了不变的和成长的心态的区别

“If you can’t reduce a difficult engineering problem to just one 8-1/2 x 11-inch sheet of paper, you will probably never understand it.” —Ralph Peck
如果你不能把一个困难的工程问题简化的8-1/2 x 11英寸的纸上,你可能无法理解他。


What does it mean to “know?” “知道”是什么意思?

Richard Feynman believed that “the world is much more interesting than any one discipline.” He understood the difference between knowing something and knowing the name of something, as well as how, when you truly know something, you can use that knowledge broadly. When you only know what something is called, you have no real sense of what it is. You can’t take it apart and play with it or use it to make new connections and generate new insights. When you know something, the labels are unimportant, because it’s not necessary to keep it in the box it came in.
费曼认为“这个世界比一个学科要有趣的多”。他认为知道某些事知道这些事的名字是不一样的,当你真正了解某事,你就可以在很多地方使用它。当你只知道某事的名字,你就没有真正的理解它是什么。你不能够把这件事单独的分离开来想,你要用它来创建新的连接、生成新的insight内心的理解视野(洞察力、领悟). 当你知道某事后,标签不重要,因为你没有必要把它放进盒子里。

“The person who says he knows what he thinks but cannot express it usually does not know what he thinks.” —Mortimer Adler
 如果一个人说他懂但不能表达,那通常是他还没有想明白。

Feynman’s explanations—on why questions, why trains stay on the tracks as they go around a curve, how we look for new laws of science, or how rubber bands work—are simple and powerful. Here he articulates the difference between knowing the name of something and understanding it.
费曼解释了:为什么问问题?为什么火车转弯的也再轨道上? 我们如何寻找新的科学定律,橡皮筋的工作原理--这些都简单有效。 他也解释了知道名字和理解的区别。


“See that bird? It’s a brown-throated thrush, but in Germany it’s called a halzenfugel, and in Chinese they call it a chung ling, and even if you know all those names for it, you still know nothing about the bird. You only know something about people: what they call the bird. Now that thrush sings, and teaches its young to fly, and flies so many miles away during the summer across the country, and nobody knows how it finds its way.”
“看见那只鸟了吗?”它是一只褐喉鸫,德国人叫它halzenfugel,中国人叫它黑喉画眉,就算你知道所有的名字,你对这只鸟一无所知。你仅仅知道人们怎么叫这只鸟。现在那只thrush唱歌,教它的小鸟飞,它夏天飞很远,穿过国家,但是没人知道它怎么认得路的。

Knowing the name of something doesn’t mean you understand it. We talk in fact-deficient, obfuscating generalities to cover up our lack of understanding.
知道名字不意味着你你理解它。我们用含糊其辞来掩盖我们缺乏理解。

How then should we go about learning? On this Feynman echoes Albert Einstein and proposes that we take things apart. He describes a dismal first-grade science book that attempts to teach kids about energy by showing a series of pictures about a wind-up dog toy and asking, “What makes it move?” For Feynman, this was the wrong approach because it was too abstract. Saying that energy made the dog move was equal to saying “that ‘God makes it move,’ or ‘spirit makes it move,’ or ‘movability makes it move.’ (In fact, one could equally well say ‘energy makes it stop.’)”
那我们应该怎么学呢? 费曼回应Albert Einstein建议我们要事情分开。费曼描述了一个低年级的科普书,里面有试图教小孩子认识关于能量的关于发条玩具狗的一系列图片,里面问“什么让它移动?”,费曼说这是错误的方式,因为这太抽象了,说能量让这只狗移动等于说上帝让它移动、精神让它动,移动的能力让它动,(实际上也可以说 能量让它停止。)。

Staying at the level of the abstract imparts no real understanding. Kids might subsequently get the question right on a test, if they have a decent memory. But they aren’t going to have any understanding of what energy actually is.
停留在这种抽象层面透露出没有真正的理解。孩子如果记忆力不错的话,在随后的测试中可能会答对。但是他们完全不知道能量到底是什么。

Feynman then goes on to describe a more useful approach:

“Perhaps I can make the difference a little clearer this way: if you ask a child what makes the toy dog move, you should think about what an ordinary human being would answer. The answer is that you wound up the spring; it tries to unwind and pushes the gear around.

What a good way to begin a science course! Take apart the toy; see how it works. See the cleverness of the gears; see the ratchets. Learn something about the toy, the way the toy is put together, the ingenuity of people devising the ratchets and other things. That’s good.”
也许我可以把这个事说的更清楚点:如果你问一个小孩什么让这个玩具移动,你应该想想一个正常的成年人会怎么回答。这个答案是你将弹簧卷起来,弹簧要伸展开然后推动齿轮转动。
这是科学课程的一个好的开头!把toy单独拿出来,看看它怎么工作。看看灵巧的齿轮,看看棘轮,学习这个玩具,玩具组装方式,人们的创造性的发明了棘轮和其它的东西。这就很好。

***

After the Feynman Technique

“We take other men’s knowledge and opinions upon trust; which is an idle and superficial learning. We must make them our own. We are just like a man who, needing fire, went to a neighbor’s house to fetch it, and finding a very good one there, sat down to warm himself without remembering to carry any back home. What good does it do us to have our belly full of meat if it is not digested, if it is not transformed into us, if it does not nourish and support us?” —Michel de Montaigne
我们基于信任拿了别人的知识和见解,这是懒惰和肤浅的。我们要把他们变成自己的。我们就像一个需要火的人,我们去了邻居取火,然后找到一个好地方坐下来烤火,然而忘记了带火回家。如果我们的肚子填满了肉,但是没有消化掉,没有任何东西转化给自己,没有任何东西滋养我们,那这么做没有任何好处。

The Feynman Technique helps you learn stuff. But learning doesn’t happen in isolation. We learn not only from the books we read but also the people we talk to and the various positions, ideas, and opinions we are exposed to. Richard Feynman also provided advice on how to sort through information so you can decide what is relevant and what you should bother learning.
学习不是只从书本上来,我们在交谈过程中也能获得各种各样的立场、想法、意见。费曼也提供了如何分类信息的建议,让你可以决定哪些相关的东西要费点心去学习。

In a series of non-technical lectures in 1963, memorialized in a short book called The Meaning of It All: Thoughts of a Citizen Scientist, Feynman talks through basic reasoning and some of the problems of his day. His method of evaluating information is another set of tools you can use along with the Feynman Learning Technique to refine what you learn.
《一切的意义:一个平民科学家的想法》这本纪念费曼的书里面记录着费曼的演讲,里面有他评估信息的方法,这是另外一些工具,沿用费曼学习法来完善你所学。

Particularly useful are a series of “tricks of the trade” he gives in a section called “This Unscientific Age.” These tricks show Feynman taking the method of thought he learned in pure science and applying it to the more mundane topics most of us have to deal with every day.
在“不科学的时代”里面记录了一系列的“交易技巧”非常有用,这些tricks可以看出来自科学的费曼学习法也是用日常生活。

Before we start, it’s worth noting that Feynman takes pains to mention that not everything needs to be considered with scientific accuracy. It’s up to you to determine where applying these tricks might be most beneficial in your life.
开始前,值得一提的是,费曼煞费苦心的提醒不是每件事都需要向科学一样的精确。这取决于你在哪使用这些技巧,才能给你生活带来更多好处。

Regardless of what you are trying to gather information on, these tricks help you dive deeper into topics and ideas and not get waylaid by inaccuracies or misunderstandings on your journey to truly know something.
无论你想在哪方面收集信息,这些技巧帮你把topics和想法挖的更深,不会被不精确、误解拦住真相。

As we enter the realm of “knowable” things in a scientific sense, the first trick has to do with deciding whether someone else truly knows their stuff or is mimicking others:
当我们进入科学意义上的“已知”食物领域时,第一个技巧要去做确定一些人是否真的知道他们的是事,还是模仿他人。

“My trick that I use is very easy. If you ask him intelligent questions—that is, penetrating, interested, honest, frank, direct questions on the subject, and no trick questions—then he quickly gets stuck. It is like a child asking naive questions. If you ask naive but relevant questions, then almost immediately the person doesn’t know the answer, if he is an honest man. It is important to appreciate that.
我的伎俩很容易使用,如果你问他聪明的问题---这是一个精辟的、感兴趣的、诚实的、坦率的、直截了当的问题,而不是诡计多端的问题——然后他很快就会陷入困境。它就像一个孩子问天真的问题。如果你问天真又相关的问题,那么很可能很快这个人就说不知道答案,如果他说一个诚实的人。
意识到这一点非常重要

And I think that I can illustrate one unscientific aspect of the world which would be probably very much better if it were more scientific. It has to do with politics. Suppose two politicians are running for president, and one goes through the farm section and is asked, “What are you going to do about the farm question?” And he knows right away—bang, bang, bang.


Now he goes to the next campaigner who comes through. “What are you going to do about the farm problem?” “Well, I don’t know. I used to be a general, and I don’t know anything about farming. But it seems to me it must be a very difficult problem, because for twelve, fifteen, twenty years people have been struggling with it, and people say that they know how to solve the farm problem. And it must be a hard problem. So the way that I intend to solve the farm problem is to gather around me a lot of people who know something about it, to look at all the experience that we have had with this problem before, to take a certain amount of time at it, and then to come to some conclusion in a reasonable way about it. Now, I can’t tell you ahead of time what conclusion, but I can give you some of the principles I’ll try to use—not to make things difficult for individual farmers, if there are any special problems we will have to have some way to take care of them, etc., etc., etc.””

If you learn something via the Feynman Technique, you will be able to answer questions on the subject. You can make educated analogies, extrapolate the principles to other situations, and easily admit what you do not know.
 如果你通过费曼技巧学到了一些东西,你就可以回答这个问题。你可以做一些有教育意义的类比,把原则外推到其他情况,并且很容易地承认你不知道的事情。 

The second trick has to do with dealing with uncertainty. Very few ideas in life are absolutely true. What you want is to get as close to the truth as you can with the information available:
第二个技巧处理不确定的事情,生活中很少有事情是确定的。利用可以用信息去无限接近真相。

“I would like to mention a somewhat technical idea, but it’s the way, you see, we have to understand how to handle uncertainty. How does something move from being almost certainly false to being almost certainly true? How does experience change? How do you handle the changes of your certainty with experience? And it’s rather complicated, technically, but I’ll give a rather simple, idealized example.
几乎确定是错变为几乎是对,,如何改变你对经验的肯定(如何否定你的经验?)

You have, we suppose, two theories about the way something is going to happen, which I will call “Theory A” and “Theory B.” Now it gets complicated. Theory A and Theory B. Before you make any observations, for some reason or other, that is, your past experiences and other observations and intuition and so on, suppose that you are very much more certain of Theory A than of Theory B—much more sure. But suppose that the thing that you are going to observe is a test. According to Theory A, nothing should happen. According to Theory B, it should turn blue. Well, you make the observation, and it turns sort of a greenish. Then you look at Theory A, and you say, “It’s very unlikely,” and you turn to Theory B, and you say, “Well, it should have turned sort of blue, but it wasn’t impossible that it should turn sort of greenish color.”

So the result of this observation, then, is that Theory A is getting weaker, and Theory B is getting stronger. And if you continue to make more tests, then the odds on Theory B increase. Incidentally, it is not right to simply repeat the same test over and over and over and over, no matter how many times you look and it still looks greenish, you haven’t made up your mind yet. But if you find a whole lot of other things that distinguish Theory A from Theory B that are different, then by accumulating a large number of these, the odds on Theory B increase.”

Feynman is talking about grey thinking here, the ability to put things on a gradient from “probably true” to “probably false” and how we deal with that uncertainty. He isn’t proposing a method of figuring out absolute, doctrinaire truth.

Another term for what he’s proposing is Bayesian updating—starting with a priori odds, based on earlier understanding, and “updating” the odds of something based on what you learn thereafter. An extremely useful tool. 贝叶斯更新

Feynman’s third trick is the realization that as we investigate whether something is true or not, new evidence and new methods of experimentation should show the effect of getting stronger and stronger, not weaker. Knowledge is not static, and we need to be open to continually evaluating what we think we know. Here he uses an excellent example of analyzing mental telepathy:
费曼的第三个窍门是认识到,当我们调查某事是否真实时,新的证据和新的实验方法应该显示出越来越强而不是越来越弱的效果。知识不是一成不变的,我们需要开放地不断评估我们认为自己知道的东西。这里他用了一个分析心灵感应的很好的例子:(主要是说平均能猜出5张牌,但是心灵感应者能猜出10-15张,几乎100%猜对。后来技术等更新,平均能猜出6.5,虽然比10-15低,但是在进步,这说明改进了方法,然后效果越来越好,是可以猜中更多的。)

“A professor, I think somewhere in Virginia, has done a lot of experiments for a number of years on the subject of mental telepathy, the same kind of stuff as mind reading. In his early experiments the game was to have a set of cards with various designs on them (you probably know all this, because they sold the cards and people used to play this game), and you would guess whether it’s a circle or a triangle and so on while someone else was thinking about it. You would sit and not see the card, and he would see the card and think about the card and you’d guess what it was. And in the beginning of these researches, he found very remarkable effects. He found people who would guess ten to fifteen of the cards correctly, when it should be on the average only five. More even than that. There were some who would come very close to a hundred percent in going through all the cards. Excellent mind readers.

A number of people pointed out a set of criticisms. One thing, for example, is that he didn’t count all the cases that didn’t work. And he just took the few that did, and then you can’t do statistics anymore. And then there were a large number of apparent clues by which signals inadvertently, or advertently, were being transmitted from one to the other.

Various criticisms of the techniques and the statistical methods were made by people. The technique was therefore improved. The result was that, although five cards should be the average, it averaged about six and a half cards over a large number of tests. Never did he get anything like ten or fifteen or twenty-five cards. Therefore, the phenomenon is that the first experiments are wrong. The second experiments proved that the phenomenon observed in the first experiment was nonexistent. The fact that we have six and a half instead of five on the average now brings up a new possibility, that there is such a thing as mental telepathy, but at a much lower level. It’s a different idea, because, if the thing was really there before, having improved the methods of experiment, the phenomenon would still be there. It would still be fifteen cards. Why is it down to six and a half? Because the technique improved. Now it still is that the six and a half is a little bit higher than the average of statistics, and various people criticized it more subtly and noticed a couple of other slight effects which might account for the results.

It turned out that people would get tired during the tests, according to the professor. The evidence showed that they were getting a little bit lower on the average number of agreements. Well, if you take out the cases that are low, the laws of statistics don’t work, and the average is a little higher than the five, and so on. So if the man was tired, the last two or three were thrown away. Things of this nature were improved still further. The results were that mental telepathy still exists, but this time at 5.1 on the average, and therefore all the experiments which indicated 6.5 were false. Now what about the five? . . . Well, we can go on forever, but the point is that there are always errors in experiments that are subtle and unknown. But the reason that I do not believe that the researchers in mental telepathy have led to a demonstration of its existence is that as the techniques were improved, the phenomenon got weaker. In short, the later experiments in every case disproved all the results of the former experiments. If remembered that way, then you can appreciate the situation.”

We must refine our process for probing and experimenting if we’re to get at real truth, always watching out for little troubles. Otherwise, we torture the world so that our results fit our expectations. If we carefully refine and re-test and the effect gets weaker all the time, it’s likely to not be true, or at least not to the magnitude originally hoped for.
如果我们要获得真正的真理,就必须改进我们的探索和实验过程,时刻注意小麻烦。我们不断的测试,使我们的结果符合我们的期望。如果我们仔细地提炼和重新测试,效果会一直变弱,很可能就不是真的了,或者至少不会达到最初希望的程度。 (总的来说就是一边试验一边看趋势。。。)

The fourth trick is to ask the right question, which is not “Could this be the case?” but “Is this actually the case?” Many get so caught up with the former that they forget to ask the latter:
第四个窍门是提出正确的问题,不是“这是不是真的?”而是“这真的是真的吗?”许多人被前一个问题缠住了以至于忘了问后一个问题:(当他是真,再来判断他是真)


“That brings me to the fourth kind of attitude toward ideas, and that is that the problem is not what is possible. That’s not the problem. The problem is what is probable, what is happening.

It does no good to demonstrate again and again that you can’t disprove that this could be a flying saucer. We have to guess ahead of time whether we have to worry about the Martian invasion. We have to make a judgment about whether it is a flying saucer, whether it’s reasonable, whether it’s likely. And we do that on the basis of a lot more experience than whether it’s just possible, because the number of things that are possible is not fully appreciated by the average individual. And it is also not clear, then, to them how many things that are possible must not be happening. That it’s impossible that everything that is possible is happening. And there is too much variety, so most likely anything that you think of that is possible isn’t true. In fact that’s a general principle in physics theories: no matter what a guy thinks of, it’s almost always false. So there have been five or ten theories that have been right in the history of physics, and those are the ones we want. But that doesn’t mean that everything’s false. We’ll find out.”


The fifth trick is a very, very common one, even 50 years after Feynman pointed it out. You cannot judge the probability of something happening after it’s already happened. That’s cherry-picking. You have to run the experiment forward for it to mean anything:
第五个技巧是非常非常普遍的,甚至在费曼指出50年后。你不能在事情已经发生之后再判断它发生的可能性。那是樱桃采摘。你必须把实验进行到底:

“A lot of scientists don’t even appreciate this. In fact, the first time I got into an argument over this was when I was a graduate student at Princeton, and there was a guy in the psychology department who was running rat races. I mean, he has a T-shaped thing, and the rats go, and they go to the right, and the left, and so on. And it’s a general principle of psychologists that in these tests they arrange so that the odds that the things that happen by chance is small, in fact, less than one in twenty. That means that one in twenty of their laws is probably wrong. But the statistical ways of calculating the odds, like coin flipping if the rats were to go randomly right and left, are easy to work out.

This man had designed an experiment which would show something which I do not remember, if the rats always went to the right, let’s say. He had to do a great number of tests, because, of course, they could go to the right accidentally, so to get it down to one in twenty by odds, he had to do a number of them. And it’s hard to do, and he did his number. Then he found that it didn’t work. They went to the right, and they went to the left, and so on. And then he noticed, most remarkably, that they alternated, first right, then left, then right, then left. And then he ran to me, and he said, “Calculate the probability for me that they should alternate, so that I can see if it is less than one in twenty.” I said, “It probably is less than one in twenty, but it doesn’t count.”

He said, “Why?” I said, “Because it doesn’t make any sense to calculate after the event. You see, you found the peculiarity, and so you selected the peculiar case.”

The fact that the rat directions alternate suggests the possibility that rats alternate. If he wants to test this hypothesis, one in twenty, he cannot do it from the same data that gave him the clue. He must do another experiment all over again and then see if they alternate. He did, and it didn’t work.”


The sixth trick is one that’s familiar to almost all of us, yet almost all of us forget about every day: the plural of anecdote is not data. We must use proper statistical sampling to know whether or not we know what we’re talking about:
第六个诀窍是几乎所有人都熟悉的,但几乎所有人都忘记了每一天:传闻不是数据。我们必须使用适当的统计抽样来知道我们是否知道我们在说什么: 

“The next kind of technique that’s involved is statistical sampling. I referred to that idea when I said they tried to arrange things so that they had one in twenty odds. The whole subject of statistical sampling is somewhat mathematical, and I won’t go into the details. The general idea is kind of obvious. If you want to know how many people are taller than six feet tall, then you just pick people out at random, and you see that maybe forty of them are more than six feet so you guess that maybe everybody is. Sounds stupid.

Well, it is and it isn’t. If you pick the hundred out by seeing which ones come through a low door, you’re going to get it wrong. If you pick the hundred out by looking at your friends, you’ll get it wrong, because they’re all in one place in the country. But if you pick out a way that as far as anybody can figure out has no connection with their height at all, then if you find forty out of a hundred, then in a hundred million there will be more or less forty million. How much more or how much less can be worked out quite accurately. In fact, it turns out that to be more or less correct to 1 percent, you have to have 10,000 samples. People don’t realize how difficult it is to get the accuracy high. For only 1 or 2 percent you need 10,000 tries.”


The last trick is to realize that many errors people make simply come from lack of information. They don’t even know they’re missing the tools they need. This can be a very tough one to guard against—it’s hard to know when you’re missing information that would change your mind—but Feynman gives the simple case of astrology to prove the point:  (人们觉得错误是因为缺少信息,甚至不知道是缺少工具)你很难知道是否错过了让你改变主意的信息。

最后一个诀窍是认识到人们犯的许多错误仅仅是由于缺乏信息。他们甚至不知道自己缺少所需的工具。这可能是一个很难防范的问题很难知道什么时候你丢失了会改变主意的信息,但是费曼给出了一个简单的占星术案例来证明这一点:


“Now, looking at the troubles that we have with all the unscientific and peculiar things in the world, there are a number of them which cannot be associated with difficulties in how to think, I think, but are just due to some lack of information. In particular, there are believers in astrology, of which, no doubt, there are a number here. Astrologists say that there are days when it’s better to go to the dentist than other days. There are days when it’s better to fly in an airplane, for you, if you are born on such a day and such and such an hour. And it’s all calculated by very careful rules in terms of the position of the stars. If it were true it would be very interesting. Insurance people would be very interested to change the insurance rates on people if they follow the astrological rules, because they have a better chance when they are in the airplane. Tests to determine whether people who go on the day that they are not supposed to go are worse off or not have never been made by the astrologers. The question of whether it’s a good day for business or a bad day for business has never been established. Now what of it? Maybe it’s still true, yes.

On the other hand, there’s an awful lot of information that indicates that it isn’t true. Because we have a lot of knowledge about how things work, what people are, what the world is, what those stars are, what the planets are that you are looking at, what makes them go around more or less, where they’re going to be in the next 2,000 years is completely known. They don’t have to look up to find out where it is. And furthermore, if you look very carefully at the different astrologers they don’t agree with each other, so what are you going to do? Disbelieve it. There’s no evidence at all for it. It’s pure nonsense.

The only way you can believe it is to have a general lack of information about the stars and the world and what the rest of the things look like. If such a phenomenon existed it would be most remarkable, in the face of all the other phenomena that exist, and unless someone can demonstrate it to you with a real experiment, with a real test, took people who believe and people who didn’t believe and made a test, and so on, then there’s no point in listening to them.”
也就是说你都不知道星座怎么在运行,怎么玩占星术?想搞清楚是不是占星术是不是真的,要做实验,如果都做了试验了,还需要占星术干啥呢?

***

Conclusion

Knowing something is valuable. The more you understand about how the world works, the more options you have for dealing with the unexpected and the better you can create and capitalize on opportunities. The Feynman Learning Technique is a great method to develop mastery over sets of information. Once you do, the knowledge becomes a powerful tool at your disposal.
知道一些事情是有价值的。你知道世界运行的规律越多,你就越能够处理好一些意外和把握住机会。费曼学习法是个伟大的办法去开发和掌握信息级。一旦你这么做了,你的知识会变成

But as Feynman himself showed, being willing and able to question your knowledge and the knowledge of others is how you keep improving. Learning is a journey.

像费曼自己所表明的那样,愿意而且能够质疑自己和他人的知识是你不断进步的方式。 学习是一段旅程

If you want to learn more about Feynman’s ideas and teachings, we recommend:



https://fs.blog/2021/02/feynman-learning-technique/
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