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.
The Feynman Learning Technique is a simple way of approaching anything new you want to learn.
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.
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.
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.
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.
Let’s go.
***
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
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:
- Pretend to teach a concept you want to learn about to a student in the sixth grade.
- Identify gaps in your explanation. Go back to the source material to better understand it.
- Organize and simplify.
- Transmit (optional).
Step 1: Pretend to teach it to a child or a rubber duck
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.
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.
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.
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.
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.
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.
Step 2: Identify gaps in your explanation
Areas where you struggle in Step 1 are the points where you have some gaps in your understanding.
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.
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.
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.
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.
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.
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.
Step 3. Organize and simplify
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.
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.
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.
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.
***
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.
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.
Not only will you supercharge your own learning, but you’ll also supercharge theirs.
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.
“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
What does it mean to “know?”
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.
“The person who says he knows what he thinks but cannot express it usually does not know what he thinks.” —Mortimer Adler
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.
“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.”
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.
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.’)”
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.
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.”
***
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
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.
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.
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.
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.
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.
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:
“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.
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.
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:
“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.
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.
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:
“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.
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:
“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:
“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:
“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:
“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.”
***
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.
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.
If you want to learn more about Feynman’s ideas and teachings, we recommend:
Surely You’re Joking, Mr. Feynman!: Adventures of a Curious Character
The Pleasure of Finding Things Out: The Best Short Works of Richard Feynman
What Do You Care What Other People Think?: Further Adventures of a Curious Character