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Steven LEVITT: My guest today, David Epstein, is one of the world’s most gifted popularizers of science. His first book, The Sports Gene, examines the role of nature versus nurture in determining who will excel in sports. And his second book, Range, touts the benefits of being a generalist in a world overrun with specialists. And if you’re looking for real world support for Epstein’s argument, that it pays to be a generalist, you need look no further than Epstein himself. He’s followed his passion in all sorts of different directions from being an elite runner in college to a scientist studying Arctic flora, to a reboot as a writer for Sports Illustrated, and now a New York Times best-selling science writer.

Welcome to People I (Mostly) Admire, with Steve Levitt.

LEVITT: I have a little problem today in interviewing David Epstein, I agree with too many of his arguments. For me, it’s most fun to have on guests who I deeply admire, but with whom I also have some disagreements. I love the uncertainty. Will my mind be changed, will the guest’s mind be changed, or will we agree to disagree? Well, even if my mind won’t be changed today, I know I’ll learn a ton because David Epstein seems to know something about just about everything.

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LEVITT: I want to talk first about your more recent book entitled Range: Why Generalists Triumph in a Specialized World. It was a New York Times bestseller. And I have to say, I think you out Gladwell’d Malcolm Gladwell with that book, which I mean as a compliment, although I’m not sure whether you’re going to take it that way or not. 

David EPSTEIN: Yeah, I don’t know. I guess it depends — 

LEVITT: Let me explain what I mean. Something odd happens to me when I read Gladwell’s books and when I read Range, the stories are so fascinating and so well-told that reading the book makes me feel like I’m witty and interesting, even though obviously I’m only a consumer of the book. I feel like I’m better than I really am by virtue of imbibing it. Does that make any sense to you? 

EPSTEIN: Oh, totally. If you read a lot, people think you’re really smart. I’m just relaying and connecting other people’s stories. So that totally resonates with me.

LEVITT: Your books are full of stories and there are a few stories in Range that I just loved. And one that really got me thinking was your discussion of the Flynn Effect. Could you just explain to those who aren’t familiar with the Flynn Effect, what that is? 

EPSTEIN: Yeah. The Flynn Effect in a nutshell is the rise of IQs across the world that occurred over the 20th century and essentially at every part of the spectrum. So scores on IQ tests shifted to the right — to higher — in every country and every part of the curve. Just as if you lifted up the scores and shifted them over. And it proceeded at a rate of about three points on IQ tests per decade. And it was the most pronounced, interestingly, on the types of tests where change was least expected. Those that were designed to be culturally reduced, meaning they shouldn’t be affected. Like if Martians landed on Earth, these are the tests we could give them because culture has no impact. It would never change. These ones that are about recognizing patterns and shapes and things like that. And that’s where actually the most change was observed.

LEVITT: Okay. So, three points per decade. So am I right that a standard deviation of IQ Is 10 points, by definition?

EPSTEIN: 15.

LEVITT: 15 points. So, our generation is one standard deviation or more than one standard deviation, smarter in quotes, on IQ tests than our grandparents were?

EPSTEIN: Smarter, yeah, that’s debated whether it’s really smarter. But on the test, yes. And just to put that in perspective, a kid who scored let’s say on certain parts of abstract tests, average today, would be in something like the 95th percentile of their grandparents.

LEVITT: That’s huge. What do you think the leading explanations are for that? 

EPSTEIN: I think it’s likely that they’re multiple contributors, but one that I think has done a good job of synthesizing a lot of different angles of the data is the idea that we have grown up with what Flynn himself calls scientific spectacles, basically. And we have been growing up in a world that increasingly prioritizes abstract thought and classification, like, instead of dealing with specific animals, we learn from a young age to start grouping them into classifications. Like I have a toddler now and I realize, even as I’m reading him storybooks, everything is about generalizing abstract ideas. There’ll be a cow that actually looks like a cow in one book and then a purple one that looks more like a human in another book. And he’s learning to connect this abstraction of a cow to all these different sorts of characters. And that turns out to be the thing that people are much better at. So if you look even at tests that just ask people to define words, there haven’t been gains really on directly observable phenomena. So, if it’s something like a chicken or illness, a kid today would score the same as their grandparents, but on words like law and pledge and citizen that are more abstract — that’s where the gains come. So it’s all on this abstract thinking. And in societies where there really hasn’t been a shift to modern work that requires abstraction, where it’s much more grounded in a deep, small body of knowledge, like subsistence farming and things like that, you haven’t seen those same gains. And that’s not to say that those people skills are not as valuable. It’s just a different kind of thinking.

LEVITT: So, when you say modern work — I’ve always imagined, like I watch my kids play video games. And video games in many ways, feel to me like a pretty good practice test for what I think would be an IQ test. But when you say work, it sounds like you’re talking about the organization of factories and office jobs, doing market research or — what do you mean by work? 

EPSTEIN: Just to add to your point of video games. The test where the least change over time was expected was called the Raven’s Progressive Matrices, which are literally these series of abstract shapes. And there’ll be a blank where one shape should be, and you just have to figure out the rules of this pattern and fill in the missing shape. And I think in a lot of ways, that is like a video game sometimes, right? Kids don’t sit down and read the instruction booklet, they jump in and try to figure out the rules. And that’s like some of these tests, but in modern work — and some of the research that influenced Flynn’s thinking was from Alexander Luria, a Russian psychologist who, when the Soviet Union was industrializing, he took advantage of a natural experiment where remote villages in what’s now Uzbekistan were being made to turn almost overnight from very traditional subsistence farming communities into collective farms. Suddenly people had to start working together and because it happened in the less remote parts first, he had control groups and saw that those people who were made to start working together, coordinating different tasks, thinking about the tasks of other people they themselves did not directly do or experience, learning how to teach things, those people started shifting to this more sort of abstract thought where they had to learn from and consider and coordinate things that they did not have daily experience with. 

LEVITT: So, what’s strange to me about this, as I’ve studied what people in the past have figured out, like gravity or electricity or quantum physics, is that past generations don’t seem to have suffered from low IQs in this regard. Do you think maybe the Flynn Effect is telling us more about the imperfectness of IQ tests in measuring true intelligence than anything else? 

EPSTEIN: That’s certainly a part of it. I always think back to some work I read by Ibn Khaldun who was known as the father of modern sociology. He was an Arab historiographer and he pointed out centuries ago that if you have a city-dweller traveling in the desert, they’d be completely dependent on the nomad to keep them alive. And that city-dweller who’s engaged in more modern work would do better on the Raven’s Progressive Matrices, but they would seem useless and not very bright in the desert. It’s important to think about intelligence and cognitive abilities being adapted to their environment for one. But also, I just think the picture is a lot more complex than IQ tests can possibly document. And certainly, like you said, if you’re talking about theoretical physicists, I would argue whenever they are operating they are outliers in terms of the amount of abstraction that they are engaging in. I’d be curious if you only looked at theoretical physicists, would we see a Flynn Effect over time and I’m not totally sure we would.

LEVITT: Yeah. If this is a normally distributed statistic and you move the mean as much as the Flynn Effect has moved it, man, you have moved a huge chunk of people into the right tail as well. But if you think about Einstein as being an outlier, there just should be tons of Einsteins hanging around these days if IQ tests are really measuring what Einstein had.

EPSTEIN: I would argue that there should be more Einsteins just by virtue of world population being larger also. And maybe there are. Einstein, obviously an epical genius, at the same time — I guess I less subscribe to the great man theory of history. And I think the findings that were made on relativity were — were something that was being built up to, and the right genius was in the right place at the right time. But that those are always things that build on one another and are often going to be discovered. And I think we do have people like that now but the problems are different.

LEVITT: So, one explanation for why we don’t see so much genius is actually the arguments you make in Range. I think lots of people would agree that we’ve moved into a period of extreme specialization. And your point is that bad things happen when people are too specialized.

EPSTEIN: I mean, talk about Einstein — he talked about this specifically, he wrote about his concern that as physicists and scientists in general became more specialized, journals would become more remote from one another. They become like these little intellectual archipelagos and you wouldn’t have people making the kind of connections that he and some of his most impactful colleagues were making across disciplines. There was way less journal specialization when he was saying that than there is now. If you look at the history of science, so many of the breakthroughs have been from people connecting knowledge that was available already — but tying it together creates a new frame. One of the great examples is Claude Shannon, who was an electrical engineer at the University of Michigan who was forced to take a philosophy course to fulfill a requirement. And in it, he learned about an almost century-old system of logic by which true and false statements could be coded with ones and zeros and solved like math problems. This had accomplished nothing in the 80 years since its creator passed away, except for getting in philosophy courses. And then he did an internship at a phone company and realized he could use the relay circuits like ones and zeros and code information into circuits. And that gave rise to binary code upon which all of our digital computers rely today. And as Shannon said, it just so happened that nobody was familiar with those two areas at the same time. And I think we lose a lot if we’re forcing people to stay in their own trench since so much of the innovation has come from connecting information across trenches.

LEVITT: But I guess the problem is that because knowledge has progressed so far, we’ve dug those trenches deeper and deeper. So, to learn the body of knowledge in just let’s say economics, because I know it, there’s a lot more economics for me to learn to get to the cutting edge than there would have been for someone in 1870. I hadn’t really thought about this even when I read your book. It’s not like necessarily we’ve made a mistake as a society. It’s just that the problem has gotten harder because if you think you have to be on the cutting edge of two different fields, to see the connection between the fields. That’s just a lot harder test than it was 50 years ago or a hundred years ago. 

EPSTEIN: Yeah, I agree. And by the way, I think we absolutely need generalists and specialists. In fact, when I looked at patent research, for much of the 20th century, actually, the individuals and teams that had the most impactful patents were diving usually more and more deeply into one area of technology. And then there was a shift when information became more widely disseminated to teams that included people who had worked in a large number of different technological classes and were often merging things from different domains. And a lot of these teams would have some specialists with depth and then these sort of connectors. Some of the advantages that I talk about that’s accrued to generalists is because there are a lot of specialists around producing useful information. We’ve also made search easier so that it’s become easier to be broader than a specialist. It’s impossible to ask people to know everything, but there are more opportunities than ever to be connectors. And I should tell you — if I’m being honest about the title of the book, like what even is a generalist? Well, it depended. When I was looking in patent research that was classified by how many different technological classes someone worked in. When I was looking in research in the arts, it was how many different genres of comic books did they work in? This was operationalized in a different way, in different areas of research. And so, I don’t even have a consistent definition of what a generalist is. What I’m saying is that you will become more and more narrow if you just let the momentum of work life take you in that direction. You have to proactively work against that to develop some breadth, or as I called a range, and that it’s worthwhile, and that there are huge benefits to that. What I think is the sub-theme of the book that would have made like a much less marketable subtitle is that — I guess I’m probably not supposed to say stuff like that, but —.

LEVITT: No, you are. On this podcast, you’re supposed to say stuff like that.

EPSTEIN: Okay. 

LEVITT: Because I say stuff like that all the time. 

EPSTEIN: Sometimes the things you can do that give you the fastest apparent progress or head start in the short-term can actually undermine your long-term development. And that’s whether you’re developing skill in sports, whether you are studying math, whether you are choosing what to major in or choosing a career or a specialty, that sometimes the things that will make progress right in front of your eyes will actually undermine that long-term development. And so to me, that was a real theme of the book. 

LEVITT: So, I was the worst kind of college student in the sense that I didn’t care about learning and I got good grades and I’m sure I was just incredibly annoying to my professors. After college, I spent two years as a management consultant. It was the first time I really had to think. And then I went back to get a Ph.D. at M.I.T. and I was smart enough to apply the lessons that I had learned in consulting, and I understood that grades didn’t matter. And what mattered was producing great research and understanding that, I went from being the worst kind of student, just caring about grades, to the best. I was pursuing ideas, relentlessly. I was trying to sample all parts of economics to figure out where I would fit. So, I started wanting to be a macroeconomist. I failed at that within like three or four months. And then I tried economic theory and I struck out again and then eventually, I settled into what made sense for me, which was studying weird topics that were actually genuinely of interest to me. Most people doing Ph.D.s don’t study what interests them, they study what other people think they should be interested in. And for me, I realized I just couldn’t function like that. I needed to really care about what I was doing. And it fits so much with what’s in your book, is because I had taken two years off I felt like I was so hopelessly and frighteningly behind my peers who were already almost finishing their Ph.D.s. And when you’re young, two years feels like forever. But in reality, what I’d learned in those two years probably gained me five years because it gave me the skills and some institutional knowledge and the perspective that my classmates, almost all of whom came straight out of college, that they completely lacked.

EPSTEIN: You mentioned most people study, not things that they’re interested in but things that they have to, that was exactly also my experience in grad school. I have a master’s degree in environmental science. I lived in a tent in the Arctic for a little while. And I loved living in the Arctic because it was cool, but I was encouraged to study something so niche because it had to be something that someone else really hadn’t studied because they were supposed to discover something new. But that ended up pushing me to a place where I was studying something that nobody else had studied because it wasn’t very interesting. And that’s where a lot of people end up. You just articulated beautifully one of those short-term, long-term trade-offs. Which is, first you sample different things to figure out where you fit. And you were behind in the short-term, but that paid off in the long-term. For me, I increasingly was asking myself, “Am I the type of person who wants to spend my whole life learning one or two things new to the world or shorter spans of time, learning things new to me and sharing them?” And I was the latter and I said, “Well, it was good to learn that, but boy, I’m behind now. And that was a waste of time.” And when I landed at Sports Illustrated as a temp fact checker, I was five, six years older than the people that I was doing that work for. Like literally, I was fact checking T.V. listings for someone who was six years younger than me. And pretty soon I realized that all these people were lining up to try to be like the next N.F.L. beat reporter, whereas I had this odd-ball skills where I was a totally average scientist at best. And then you bring that over to a sports magazine and suddenly I’m a Nobel Prize winner. And realizing this thing that was totally ordinary in one place is suddenly totally extraordinary in this other place. And that if I could do something interesting with it, I wasn’t even competing with anybody. I quickly zoomed past and became the youngest senior writer there by creating this area. I viewed myself as a consummate generalist, whereas my colleagues in sports writing viewed me as the specialist, because I was the science writer in sports.

LEVITT: Uh huh, yeah. Interesting.

EPSTEIN: Which is funny. That’s what I’ve done through my whole career is basically take something that’s ordinary among my peers and then move it somewhere where it’s more rare. So I’m curious to ask you, how did you know so quickly or how did you know at all that you weren’t a fit for those things? 

LEVITT: For me, it was just really obvious. I had always done well in macroeconomics courses as an undergraduate, but I had no intuition for it. I wasn’t as bad at theory as I was in macro, but I did — this is one of the most embarrassing stories I have about myself but I’m going to tell it anyway. So, I’m pretty socially awkward. My adviser, when I was trying to do micro theory, the third time that I sat down to meet with him, he called me David instead of Steve. And any normal person on the planet would have said, “Oh, my name is Steve,” but I didn’t. And then he kept on calling me David. And so there was a — what we called the Skit Party at M.I.T, where all of the students and the faculty would get together. And before the Skit Party, I rounded up all of my friends and I said, “Look he’s going to call me David. Just don’t worry about it. Just let it go. He calls me David.” But eventually, after about six months the stress of knowing that someday he was going to figure out that I was not David and he was going to ask me, “Well, why have you not ever corrected me when I’ve called you by the wrong name for two years?” I felt like I had to quit theory based on that. Now, that is a terrible reason for quitting theory, but I’m very thankful that it happened, because there’s no doubt that I was better at doing data work than theory.

EPSTEIN: That’s the best career switching story I’ve ever heard. That you got too far into letting him call you the wrong name and you simply could not bear. Well, it worked out. 

LEVITT: In the context of our current educational system, the features that I think feed into exactly what you’re talking about is way too much memorizing of facts and learning rules for solving math problems, without understanding even why those rules work. If you want a kid to be able to solve a set of math problems, the easiest way to do it, it’s just to say, “Look, you don’t need to understand it, just do step A, step B, step C and it’ll work.” And as long as the only kind of problem they see is that exact problem, they’ll be the Michael Jordan of solving that problem. But as soon as you give them a slightly different problem, they’ll be terrible at it. I saw it in my kids so much growing up and that’s a piece of the education system I’ve really been rallying against, but what do you see in the educational system that you would change to get away from this shortcut to the top that turns out really to be a false promise?

EPSTEIN: Well, to give an example of that short-term, long-term trade-off, a study that came out too recently for me to get it into my book randomized different types of math learning. Some got what’s called blocked practice, where you get problem type A-A-A-A-A, then B-B-B, and so on. And the students make quick progress. The other classrooms got randomized to what’s called interleaved practice, where it’s like, as if you took all the problem types and threw them in a hat and drew them out at random. The students are more frustrated. But instead of learning how to execute procedures, they’re learning how to match strategies to a type of problem or the structure of the problem. And then when all the classrooms got the same test, where now they were facing problems that they had not exactly seen before so they had to transfer some of their knowledge, the interleave group blew the block practice group away. Studying the same stuff, just an order that slowed them down and made it more difficult and more frustrating but forced them to learn some more general skills — it slows learning down, but makes it a lot more effective. Frustration is not a sign that you aren’t learning, but ease definitely is. And the fluency of learning experience is a really bad indicator of how well you’re learning. And I think that’s just an incredibly hard thing to sell to parents and students and teachers.

LEVITT: The people I know who study learning say exactly that. If you’re not frustrated, then you’re not learning. But it’s interesting, that is not a lesson that has pervaded, at least my psyche, I don’t think the American psyche more generally. I think we’ve done just the opposite. 

EPSTEIN: I’m curious to hear — so my challenge in writing both of my books has been — if I were just allowed to read and learn the things that I had to read and learn for my books and then email my friends about the interesting stuff I learned, that’s what I would do. Instead of trying to form it all into some kind of coherent 90,000 word thing — I mean, I have a digressive brain as it is and my books are me being organized and they’re still digressive. And so I find the challenge of having to organize the information to be incredibly frustrating, where sometimes I just have to step away because I’m just like overheating from frustration. And yet, it forces me to learn the material in a way that I never would if left to my own devices just for fun. I’m curious — for you in some of your projects, are there parts that you find frustrating but that you’re glad you’re forced to do from a learning perspective?

LEVITT: It’s funny, I’m not good at writing down economic theory, but I enjoy it immensely because it’s very much a flow state for me. In like, the puzzling of trying to make a model that’s internally consistent and talks about what I care about, that for me actually turns out to be a really interesting exercise. It’s a frustration that crosses over into obsession. And I think if you can take your frustration and then become so obsessed with finding the answer, that’s how I deal with frustration. Or I quit. I do one of two things: I either quit or I become completely and totally obsessive and can’t focus on anything else in my life. 

You’re listening to People I (Mostly) Admire with Steve Levitt, and his conversation with journalist David Epstein. After this short break they’ll return to talk about the importance of guesstimation and how David debunked the 10,000 hour myth.

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LEVITT: So, today’s listener question comes from Geoff. And Geoff writes, “I’ve long thought that a lot of the value universities offer is credentialing and is thus accrued upon acceptance or attendance. M.B.A. programs, I think, are an even more extreme example of this than four-year institutions. So, a simple question for you, Steve Levitt: Should I go to the effort to apply to, enroll in, and go to business school with the plan of dropping out after the first semester? I’m seriously asking this question, I really don’t want $200,000 in debt, but I have fear of missing out on a great networking and credentialing opportunity.” So, Geoff, honestly, this sounds like one of the worst ideas ever. And let me explain why. Your two goals are to get credentialed and to network. So on credentialing, I think attending one semester of business school would actually be interpreted as a huge negative signal by employers. Virtually no one drops out of M.B.A. programs, so I think the logical response from an employer would be to wonder what’s wrong with you that you couldn’t handle business school. On networking, I think if you dropped out without explaining why, your classmates would also view you as a loser. And if you told them your plan that you were just trying to game the system so you could add them to your network, they’d treat you like a pariah. So really, I think pursuing this plan would leave you deeply disappointed. But, Geoff, I will say this, even though I think this is a terrible idea, it’s an idea. And in my opinion, a bad idea is way better than nothing. My experience is that anyone who has lots of ideas will have lots of bad ideas along the way to a few good ones. And those ideas will serve you very well in life, even if this one is not one of my favorites. Geoff’s email did, though, get me thinking. Why don’t people ever put on the resume things like, “Accepted to Harvard Business School but chose not to attend,” or “Accepted to Princeton, but attended Ohio State because I received a full ride scholarship”? It would be interesting, though, if a listener who had passed up on an opportunity at a high prestige academic institution did a little randomized experiment. If they included that information on some job applications and not on others to see whether it affects the likelihood of getting a first interview. I’d be really interested to see how that turned out. So please keep your questions coming. The email address is pima@freakonomics.com. That’s P-I-M-A at Freakonomics-dot-com. And now back to the conversation with David Epstein.

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LEVITT: In the second half of our conversation, there’s still a little more ground to cover on generalists versus specialists, but mostly I want to turn our conversation towards the magical 10,000 hour rule regarding how much practice it takes to become elite at just about any activity. I think many listeners will be shocked to hear that David Epstein destroyed the 10,000-hour concept in his first book, The Sports Gene

LEVITT: What I took from your book was that there are a lot of forces, parental and societal, that push people towards being quite specialized in what they do. And your argument was that both from a societal and from a personal perspective that fighting against that tide and trying to push yourself to be more general for many or most people would be a good idea. Is that a fair assessment or do you think I’m off base with that? 

EPSTEIN: No, I think that’s a fair assessment. When I was at Northwestern spending some time with a woman named Dedre Gentner who’s one of the world’s experts in analogical problem solving — so using analogies to solve problems, especially novel problems, things that maybe nobody has seen before. So, drawing on analogies becomes a useful problem solving engine and long-story short, she and her colleagues created this test that tests whether students are good at solving novel problems. And they would structure these problems in different ways. And what she basically found was that most of the students were good at solving problems that looked like things that were very familiar to them from their major. But then not good at solving even problems that were basically the same problem, but just with different window dressing of a different domain. Their problem solving skills were very narrow. They weren’t recognizing similar problems in other areas. The exception to that was these students in this program where they had no major, they just had a bunch of different minors. Essentially, they would dip a toe into each of the sciences and see how those areas examine problems, how they approach problems, what different tools they use. Those were the students who did the best at solving novel problems. But when I went around and talked to her colleagues, they would say, “Oh, we don’t like that program because those kids are getting behind.” 

LEVITT: That just hits on a point which resonates with me, which is whatever I do in the classroom does a terrible job of helping the students to be able to generalize the specific lesson I taught them to other settings. Let me give you the best example I ever had. So I was teaching the M.B.A. class on experiments in firms. I would just go through and document dozens, 50, 75 different randomized experiments that have been done in firms with the idea of giving them examples that they could build off of. And we went out for drinks after the last lecture. And I said to one of them, “Hey, do you think you apply this in your job that you’re doing over the summer?” And he said “No, I really like the class, but it’s just not relevant to what I’m doing.” He said, “I’m working at a soft drink company in the marketing department.” And I said, “But wait a second. Two of the experiments we talked about were marketing experiments done by Budweiser. Why wouldn’t you do those?” He said, “Oh, those are in the beer industry. I’m in the soft drink industry. Those wouldn’t really apply.” And I just thought, oh my God, are you kidding me? It was the last time I ever taught M.B.A.s. I thought “I’m not going to teach M.B.A.s if that is how little I’m able to teach them.”

EPSTEIN: I mean, if we’re viewing knowledge as generalizing that little, then I guess they assume that whatever they learn at one of their jobs is just wasted as soon as they move on to the next one.

LEVITT: I’ve completely changed the way I teach my undergraduates. I used to focus intensely on emphasizing facts, detailed knowledge of the methods, and the conclusions of a particular research paper or a specific mathematical techniques. But the students from the first year that I taught classes, came back for their five-year reunion and we got together again. And I asked them what they remembered from my class. And the answer was almost nothing. They couldn’t remember basic facts when I quizzed them. I would give them easy math problems. They couldn’t solve those. The only things that were sticking with them, at least at a conscious level, were things that were different and outrageous and salient. 

EPSTEIN: It reminds me of my own education. In retrospect, when I became a grad student, I was rushed into learning all this didactic information about Arctic plant physiology and just cramming my brain full of papers and learning all the chemical cycles and things like that. None of which I remember now. Or very little. And what I didn’t learn was how scientific investigation and thinking is even supposed to proceed. I guarantee you that my master’s thesis, which is still published in a peer-reviewed journal, if it’s a true finding then that was by luck. Now people talk about the replication crisis, not that most people have necessarily heard of that, but just that a lot of published science is not true because of poor methodology and statistical analysis on the part of the scientists. And I was in that camp where my initial hypothesis about what was going on in the carbon cycle in the lower Arctic was not confirmed by my data, but I had a huge amount of data so I just started running all kinds of statistical tests on it. And a whole bunch of other stuff popped out as real. And so then I published some of that stuff, but totally unwittingly. Like I had no idea that when I pressed enter on the statistical program, what it was even doing, other than telling me if I got some statistically significant results or not. I don’t mean to suggest that facts are not useful, that people shouldn’t know facts. But I think the question is, how can they use them? The research suggests that they only are really usable once they’re embedded in a semantic network that makes them available for someone in problem solving. And that makes sense of them in relation to the other things in their brain. 

LEVITT: You’re really saying we need to teach people how to think in some sense is what I’m hearing, but do you have ideas about how we should do that better? 

EPSTEIN: I wanted to make clear in the book that I did not think we don’t need specialists. And so in the last chapter, I focused on doctors and scientists who in the scope of humanity by any stretch are specialists. And looked at Arturo Casadevall who is one of the most influential immunologists in the world. The chair of immunology at Johns Hopkins. He’s in one of the most specialized niches in all of science and went there because he realized he wants to start a program to de-specialize the training of future scientists, where you put off all that didactic stuff. As he says, people are walking around with all the world’s knowledge on their phone and no idea of how to integrate it or what to even look for. He’s saying, “Hey let’s start the program with, how does scientific thinking work? Let’s look at famous errors in scientific thinking. Let’s try to poke holes in things that are already published. Let’s look at how accidents and experimentation led to innovation in the past.” And so I think there should be really a focus on scientific thinking, not for science, but just that method of thinking. I mean, I think it’s so important even for analyzing the news.  

LEVITT: One thing you mentioned in Range is the value of being able to make back of the envelope calculations. And that’s something we generally don’t teach in school. I wonder why. 

EPSTEIN: I’m a big fan of Fermi estimation. Basically, it’s like total back of the envelope estimations to start thinking about a problem, named for Enrico Fermi, who created the first sustained nuclear fission reaction. And he used to have people do this kind of estimation, which instead of going with your gut reflex, if you can break the problem down into a lot of pieces and make very rough estimates about each of those pieces, then you don’t actually have to be very accurate about any one to come up with an answer that is often in the right order of magnitude, or at least show you if your first gut instinct was totally ridiculous. I was reading news about lotteries to incentivize people to get vaccinated in different states. And it’s an interesting idea. And, of course, the one that came all over my Twitter stream, was the one in West Virginia where they’re giving out some guns, I think 10 guns or something like that to incentivize people to get vaccinated. And I have to say, my very first gut instinct to that was like, is this an Onion article? They’re giving away guns for vaccines? But having spent long enough evaluating my own reflexes and treating my first reflex as a hypothesis that I then want to try to falsify, made me stop and say, “But is this a good public health trade-off, that’s the question I should be asking.” And I said, “Okay, I know there’s something like a gun or a little more than a gun per person in the United States. And something like 5.5 or 6 million new infections if, given like the daily rate, when I looked at it, over a year, and maybe 1 or 2 percent of people who are infected will die and we have about 150 million unvaccinated people.” And pretty quickly with those basic numbers, I can see, “Oh, Covid is way deadlier than guns.” And so it just made me stop and challenge my initial reflex and to view my initial reflex as a hypothesis in need of testing and to go from there, to start attacking the problem by just making these very broad estimates. And very quickly, it becomes clear, if you’re considering deaths from a public health standpoint, there’s no question that this trade-off is worth making. And I think that kind of thinking that can equip people to put some friction between their intuition and their conclusions, whether they’re working in an area that requires that kind of thinking or just consumers of the news, is important. 

LEVITT: When I read in your book about how important you thought guesstimation was, it just reminded me how, like you, I’ve used these tools on a daily basis. My entire life they’ve been so valuable to me. So how is it possible that we don’t teach every kid in America guesstimation? It just seems like such a sensible idea but it doesn’t fit into anybody’s curriculum. And so it just doesn’t happen. 

EPSTEIN: It doesn’t. And I think it can be taught at different levels, because you become better at it too. Once I became oriented to it, it started making me want to accumulate a sense of numbers when I read the news. It’s spurred my curiosity about knowing numbers, in general, because it feeds back into making you better at estimation. It’s hard for me to think of a better skill that we need across society for the modern consumer of information.

LEVITT: Another thing we don’t teach in school very much is good taste in tackling problems. I’m not sure that makes sense the way I said it. Let me be more concrete. Reading your books over and over, my reaction was, “What a great story. David Epstein has great taste in picking his subjects.” How do you know whether the subjects you pick will be interesting to people and they’ll capture their imaginations? 

EPSTEIN: Gosh, that is a really tough question. You reminded me of — I worked on one This American Life story, and I remember reading that Ira Glass had said, “I knew early on that I was bad at the stuff that I was doing, but I had good enough taste to know I was bad.” If you don’t realize you’re bad then you’re in trouble. I definitely allow myself to be guided by my curiosity and what fires me up. The Sports Gene, my first book, was largely composed of questions from watching and participating in sports that accumulated in my head that I wanted to seek answers to. Why are certain populations so overrepresented in certain sports? Why can’t baseball hitters hit a softball pitcher and those sorts of things. 

LEVITT: I think it’s interesting. You’re just saying, you just are yourself and you pursue what’s interesting and exciting to you. 

EPSTEIN: Yeah, I think so. I guess I’m trying to think of if I have anything — because it sounds like, so not useful, something I just said there, like I just looked for the ones I’m interested in. 

LEVITT: That is useful. Look, it’s true. Sometimes the truth doesn’t come in the form of a published study by some famous sociologist. Sometimes the truth is just what’s true. I think that’s a really great answer. 

EPSTEIN: I guess one thing that has been useful for me, I’m pretty good about avoiding the sunk cost fallacy a little bit. I will go down some rabbit hole that I think’s really interesting. And sometimes it just turns out that it’s not, or just on deeper reporting things I thought were true are not true, and I abandon them. 

LEVITT: So, in some ways, your first book, The Sports Gene was a 300-page debunking of the 10,000 hours argument, or at least an argument that the 10,000 hours mantra is way too simple. Is that an accurate reading? 

EPSTEIN: Yeah, definitely. I think the most popular conception is that there’s no such thing as talent. It takes 10,000 hours of so-called deliberate practice — this is effortful, cognitively-engaged, error-correction-focused practice to become an expert in anything and this is based initially on the work most prominently of a man named Anders Ericsson. The original work was at a world-class music academy and it studied 30 violinists. So a small study to start with, but the 10 best violinists, those who the instructors deemed capable of being international soloists, had spent on average 10,000 hours in deliberate practice by the age of 20. And the next two groups down, lesser. The first thing that raised my eyebrow about that paper was that there was no measure of variance included. So my question was, “What was the range of this?” Because I had read other expertise research, like in chess, where it took 11,053 hours on average for people to reach international master status, which is one down from Grandmaster. But some people made it in 3,000 hours and others were being tracked at 25,000 and they still hadn’t made it. So you could have an 11,053 hours rule, but it didn’t really tell you much about the reality of skill acquisition. And so that was the first thing that made me skeptical. When I asked after that data, the responses I got were basically like, “People were inconsistent on their retrospective accounts of how much practice they did and we’ll have better measures of variants when we have video diaries that people can keep.” So the answer is that your data aren’t very good? Like lots of people face that problem, but they still have to include measures of variance. And then as I started to get into more research about skill acquisition, what I was realizing was that it was the rate of learning. So there was something underlying the 10,000 hours rule called the monotonic benefits assumption that basically meant that every person at the same level of skill should progress the exact same amount for the same hour of deliberate practice. And that turns out to be true, essentially, nowhere, except in very simple perceptual motor skills. As an example, in some research on air traffic controlling, if it’s a really simple task, like someone just has to click a mouse really quickly to like, move planes to let other ones land, everyone will gravitate toward the same skill level at about the same rate. But as it gets even a little more complicated where you have to balance multiple things at once, people actually diverge. Some people improve much faster with practice and pull away. And that turned out to be much more the rule in human skill acquisition. These rates of improvement differed gigantically between individuals, which made seeking out the place where you improve and learn more quickly, something that I think is important to encourage people to do. Whereas, in the 10,000 hours thinking, that makes no sense. You should just pick the first thing that comes and stick with it. 

LEVITT: So, there are two things — there are differences in initial skill at something, and there’s also differences in the rate at which people improve. I think a great example of that is your own experience with track. Could you tell your own story about you as a runner?

EPSTEIN: Yeah. I was a walk-on 800 meter runner in college, meaning I wasn’t good enough to get recruited. And I got paired with, you know, a blue-chip recruit, this guy named Scott, who was already on the Canadian national team. He was 20 seconds faster than I was when we were both juniors in high school. Like you would put your head down because you’d be embarrassed for someone if you saw those two people on the same track.

LEVITT: So, 20 seconds is a lot. 

EPSTEIN: Yeah. 

LEVITT: How long does it take to run 800 meters? 

EPSTEIN: In the vicinity of two minutes, basically. But I stuck with it and I had kind of a blessing in disguise, which was that nobody cared what I was doing. And I also paired up with an older runner who was willing to be a mentor. And over the next two years, we basically experimented with my training. And at first I got worse experimenting with my training, but we would triangulate what seemed to work and what didn’t. And in my third season where I had training that was more tailored to me, I started improving like every race and I beat Scott for the first time. He never beat me again. 

LEVITT: He was the superstar Canadian.

EPSTEIN: Right. And it led to all these interesting narratives foisted upon us, like, “He was the blue-chip recruit, all this talent, not getting better. So he must be like a headcase.” And for me it was, “Oh, walk-on, no talent, but you keep getting better. Ah, he’s so tough.” But I had time where I didn’t really have to perform right away. And my story, I think, exemplified a significant body of research in physiology that shows that in improving some of the physical systems that are needed in the kind of running I was doing, the correlation between your ability at baseline and your ability to improve is small or basically zero. And that your trainability is actually the most important thing. And so I was what they would call low-baseline, high-responder, where Scott was probably more like a high-baseline, low-responder. And the people who are Olympians tend to be high-baseline, high-responder. But it was really that we’re used to viewing talent as what we see right now. And assuming people are on a stable trajectory and that’s not the case.

LEVITT: So, I started our conversation by comparing you to Malcolm Gladwell and the 10,000 hours rule that you’re taking a part in your book, The Sports Gene, that was popularized by Gladwell in his book Outliers. Don’t I remember you and Malcolm Gladwell having a highly publicized debate about that theory? 

EPSTEIN: Yeah, the first time we ever met was after The Sports Gene came out. We were invited to the M.I.T Sloan Sports Analytics Conference to debate athletic development. And so I read some of his writing about the primary importance of a head start in so-called deliberate practice. And I was the science writer at Sports Illustrated at the time so I went and looked at the literature and saw that, in fact, in most studies, the athletes who went on to become elite, actually had less deliberate practice early on in the sport in which they had become elite. More sort of free-form activity, wider variety of physical activity. And to his credit, when we were coming off the stage, he said, “It doesn’t comport with my conclusion. You want to go running tomorrow? And talk about it?” We’d both been competitive runners. And so we started kind of arguing about it our own time and that in some ways became the seed of Range. And we went back and did this again at the Conference in 2019. And in that case, at the very end, he says something like, “Yeah, I think I made an error where I conflated the fact that a lot of practice is important to become great, which I think is true with the idea that that implies you need early specialization, which I now think is false.” And so I think, honestly, he and I are kind of on quite similar ground now. 

LEVITT” So, you probably don’t know that Malcolm and I once had a similar debate. This was back in 2006 and Malcolm had argued in The Tipping Point that innovative policing was a reason that crime had gone down in the 1990s. And I had argued in Freakonomics and my academic work that legalized abortion was much more important. And obviously I’m hopelessly biased, but I think I won the debate. A year later, Malcolm had to write a little blurb about me when I was named to the Time 100 group and Malcolm referenced our debate in describing me. And here’s what he wrote: “It was a straightforward back and forth. Levitt got up and made his case and I got up and made mine. But halfway through I glanced over at Levitt and I had a realization that I’m not sure I’ve ever had before with an intellectual opponent. That if I made my case persuasively and cogently enough, he would change his mind. In other words, he was listening.” That was one of the highest compliments I think anyone’s ever given me. And it’s so interesting to me that a decade later, Malcolm actually did the thing that he suggested I might do, which is he conceded to you. He listened to you and he was convinced. That’s really impressive. Both the fact that you were cogent and persuasive enough to do it and that he was willing to be swayed. 

EPSTEIN: I do think he could have, being the stature of writer and public intellectual that he is, he could have just tried to brutalize me at that debate. And I think he came into it wanting to have an interesting conversation and that sort of set a tone for me, that I think has made it easier for me to realize that being willing to learn from my critics is both a competitive advantage and just a more satisfying approach. If there’s anything I identify with being a good generalist, it’s epistemic humility. You have to be humble and ready to update your models. These questions that I’ve written about in my books like the balance of nature and nurture in developing a skill, how broad or specialized to be — everyone has these conversations implicitly or explicitly and usually only with their intuition. And so the highest goal I have is — can I bring some stories and research to those conversations and make them more interesting and productive and help people update their mental models? And I’m going to — certainly going to keep updating mine. And that’s kind of the best I can hope for. 

LEVITT: I find myself in the situation of sometimes being a specialist, and other times being a generalist. When it comes to, say, research on crime, I’m no doubt a specialist. I’ve spent many years of my life thinking deeply about the issue, I’ve analyzed the data from every angle I can come up with, and I’ve published dozens of academic papers. On many other issues, say, for instance, climate change. I absolutely come to the problem as a generalist. So here’s what’s interesting to me as a crime specialist, I haven’t had a really innovative idea in that area in at least a decade. Somehow I used up all my ideas. It’s hard for me to see things with fresh eyes. But I definitely wouldn’t want to debate me on a crime-related subject. My stock of knowledge is immense. And thinking back to that debate I had with Gladwell on crime, I was shocked he would accept the invitation. It was clear to me that there was no way he, as a generalist, could have any hope of holding his own against a specialist in the area. On the flip side, I actually think I do have some good ideas on climate change where I’m not a specialist. For instance, I did a Freakonomics Radio episode called “The Simple Economics of Saving the Amazon Rainforest.” It’s episode 428 if you want to check it out. I believe the idea put forth in that episode is one of the most straightforward, important steps we could take to fight climate change. But here’s the thing if I had to debate a climate scientist, I’m sure I would get destroyed. And that’s true even if my idea is really, truly outstanding. As a consequence, it’s extremely hard for generalists to get anyone to take their ideas seriously, even when they happen to have a big platform like I do. It seems to me that as a society, we should put more emphasis on ideas. Coming up with them, exploring them, and implementing them. So, I’m going to take as my homework from this episode to think about what more I might be able to do to help non-specialists find an outlet for their ideas. If you have any thoughts on that topic, please send them along. I’d love to hear them. The address is pima@freakonomics.com. And I read every email you send.

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People I (Mostly) Admire is part of the Freakonomics Radio Network, which also includes Freakonomics Radio, No Stupid Questions, and The Freakonomics Radio Book Club. This show is produced by Freakonomics Radio and Stitcher. Morgan Levey is our producer and Jasmin Klinger is our engineer. Our staff also includes Alison Craiglow, Greg Rippin, Joel Meyer, Tricia Bobeda, Zack Lapinski, Mary Diduch, Brent Katz, Rebecca Lee Douglas, Emma Tyrrell, Lyric Bowditch, and Jacob Clemente. All of the music you heard on this show was composed by Luis Guerra. To listen ad-free, subscribe to Stitcher Premium. Thanks for listening.

LEVITT: So, I do really the lowest status economics. In the hierarchy of economics, you get to Freakonomics more or less at the bottom. 

EPSTEIN: But podcasting makes up for all the status. 

LEVITT: Oh yeah. That probably denigrates me even more.

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