Sam Behseta

Howard Wainer’s Visual Revelations column is arguably the longest-running serial publication in the history of statistics: 25+ years of delving into the core of events of the time, all from the point of view of a razor-sharp, uncompromising, and greatly responsible statistician. Now, that is a career unto itself, but then there are some 400 articles (including articles in CHANCE), volumes of books, and years of leadership at the Educational Testing Service and National Board of Medical Examiners—not to mention a long teaching career at the University of Pennsylvania, among other things! In recognition of his extraordinary contributions to CHANCE and the profession in general, former CHANCE Executive Editor Sam Behseta sat down with Howard for a friendly chat. This is what they talked about.

Sam Behseta: Howard, how did you become involved with CHANCE?

Howard Wainer: I got a call from Steve Fienberg after he and Bill Eddy started CHANCE. He knew of my interest in graphics and said that there’s a column called Visual Revelations that Alan Paller had originated. Alan had written the first year or two’s worth but soon he was to be leaving, so Steve asked if I would take it over for a while. I thought about it. I had ideas for about three columns, and that should carry me through most of the first year. And surely I would be able to think of something else for the fourth one. So I figured I could certainly take it for a year and with luck I could get through two. So I agreed.

Howard Wainer, who writes Visual Revelations, is distinguished research scientist at the National Board of Medical Examiners. He has won numerous awards and is a Fellow of the American Statistical Association and American Educational Research Association. His interests include the use of graphical methods for data analysis and communication, robust statistical methodology, and the development and application of generalizations of item response theory. He has published 20 books so far; his latest is Medical Illuminations: Using Evidence, Visualization & Statistical Thinking to Improve Healthcare (Oxford University Press, 2014).

Every year since I thought, “Well I’ve got another idea for one more column,” and so it just kept going. A bit like mathematical induction—if I had n-columns, I could always dream up one more. Anyway, I have enjoyed it so much that I just kept going. It has quickly been 25 years.

Cutting back on professional activities has been a constant source of conversation between my wife Linda and me for several years, both what will I cut back on, and when. She would often ask, “Well, are you going to cut back on consulting?” I would say, “No, I like consulting.” “What about your full-time job? You don’t have to work full time,” she would say. And I would say, “No, they pay me; I can’t do that.” And so the conversations always repeated.

But this year I said, I think maybe CHANCE is something I can stop. She asked, “Why?” I said, “It’s been 25 years.” And she asked, “So? Does it take a lot of time?” I said, “No, not if I have an idea.” She then asked, “Why don’t you keep going and then if you don’t have an idea, then stop?” So we’ll see. My plan is that when I start drooling over my shirtfront, it’s time to quit.

Sam Behseta: So what’s the process for you? How does this work? I mean obviously you look around and observe and you have your professional stuff, but then how does it work? How do you decide that a piece is suitable for CHANCE?

Howard Wainer: To some extent it’s like relying on the kindness of strangers. I’ve relied on the kindness and wisdom of the editors, and the editors of CHANCE have been both wise and generous over the course of all these years. I figure about one idea out of every 50 is any good. Some people get one idea a week, and so they get one good one a year. John Tukey said that he used to get 50 ideas before breakfast and although even he—he claimed—had only one good one out of 50, it didn’t matter because he had plenty of them.

I don’t have 50 before breakfast, but I have a bunch of them. But there’s a filtering process that goes on. I pick one that I think will be good, and then I draft something and send it to the editor and ask is this something that’s suitable? Obviously, I’m relying both on wisdom and generosity to say yes it is or no it isn’t. You in particular have been enormously generous because the range of things that I’ve written about during your tenure has been reasonably broad. It’s long past when I stuck to the original notion of only graphics, except graphic in the other sense of literal life-likeness.

At the moment I feel I’ve satisfied my obligation to you because I finished up your last issue, but alas I haven’t got a clue what the next issue is going to be. I’m beginning to think more about cancer clusters and the hot hand in basketball, so maybe that’s going to grow into something interesting.

Also, I’m working with several teachers who have been fired because of alleged cheating, and the statistical errors in the prosecution are overwhelming. I thought that might be an interesting set of topics. Despite no plan to do so, I have become like Rumpole. I always defend. Thus, I have a few things percolating, and whatever comes up that seems complete and that I can get permission to write up will be next. So we’ll see how it happens.

Sam Behseta: Is there any cause célèbre that you see as a source of future topics?

Howard Wainer: I’m becoming more and more convinced that it’s going to be useful to take a more publicly Bayesian point of view now. I’ve been reading more about what is called the prosecutor’s fallacy. Basically it’s calculating the wrong probability and not knowing how to do the Bayesian flip (or not realizing that it should be done). Maybe that will be worth describing. I have some terrifying examples.

But the image of the initial audience for my column that I have in mind is often that of the Stat 101 teacher who’s trying to present an idea to their class to illustrate a statistical principle. It has to be something that they can do in one class without much math beyond algebra. The joke I made earlier about being able to read it in the bathroom is pretty close to the goal. My vision is that an instructor could go through the various columns and pick out a topic—a case study that they can use to illustrate something that the students can read and get some idea of how exciting it is to be in the field that we are in.

At the 2013 Joint Statistical Meetings (JSM), when Nate Silver was speaking, two things occurred to me. One was how angry I am with him. It used to be, whenever I traveled, I could sit on a plane and read. If whoever was sitting next to me asked what I did, I’d say I’m a statistician and that ended the conversation. And so I had a lot of peace and quiet. Now after having been a nerd for 40 years, suddenly I’m hot. Nate Silver has made being a statistician cool. It’s fun to look at him because he’s a nerd, too.

Sam Behseta: Now people want to talk to you.

Howard Wainer: Right, it’s, “You’re a statistician—that’s way cool.” Aaaargh!

Sam Behseta: Right. What I noticed in your pieces is that there is a sense of justice. You’re interested in social justice largely speaking—at least [in] the pieces that I’ve edited or worked with you on. The piece I really liked was value-added models. Where does that come from? Do you feel a responsibility to comment on social issues? Or it just comes with it?

Howard Wainer: I’m not sure. If I had to say I had a religion, it’s the worship of evidence. You look at the evidence and follow where it leads. I guess that’s what you might call a weak prior in some important sense. In addition, my parents were children of the Depression, and so FDR was much closer to being a deity than a politician. So the 12th commandment, certainly in my family, was thou shalt not vote Republican. So I more or less absorbed democratic as well as Democratic ideals as part of mother’s milk.

Sam Behseta: If that was the 12th commandment, what was the 11th?

Howard Wainer: Thou shalt not buy retail.

Since my childhood, I have seen that evidence overwhelmingly says that the idea of living in a society in which some people benefit enormously and others don’t, for no good reason, neither makes any sense nor is it sustainable. You can’t have a society that lives that way for very long because sooner or later people at the bottom end catch on and object in a very strong way.

I think we’re certainly approaching that now. People who have a great deal have been getting a great deal more, and people who have very little are getting less. Any rational person would say, if they’re at the short end of that, “I’m going to object to this in some serious way.” So that kind of comes out I guess.

There’s also this notion that the pathway to heaven is not going to be opened by the size of your bankbook. There are going to have to be other things. Some people, like teachers, do God’s work as an obligation of their profession. But others, for example those who buy and sell companies to one another, have to seek salvation outside of their daily work, and so they get into philanthropy and such.

We as statisticians understand data and we tell stories. That’s what a successful statistician does. And that’s what I do. The French have this wonderful expression, “Of course God will forgive me; that’s his job.” What do we do? We look at evidence and tell people about it; that’s our job.

Sam Behseta: So you were in Chicago in the ’60s? I was talking to Steve Fienberg yesterday. He was saying that you were more or less there at the same time.

Howard Wainer: Yes. We came pretty much together. He was there a year ahead of me in ’69, and I came in ’70.

Sam Behseta: But you didn’t study statistics?

Howard Wainer: I was a faculty member, not a student. I was in something called the Committee on Methodology in the department of behavioral sciences.

I met Steve early in the fall of ’70; I had just arrived a few months before. They recruited two young assistant professors in statistics to count the votes in the faculty senate election. They use the Hare system, which is complicated because it is iterative. So there were Steve and I with all these ballots. We had to arrange them in certain ways before we could start calculating the outcomes via the Hare system.

While we were doing this, I was complaining mightily about what a dopey waste of time this was. Steve turned to me very sternly and said “Stop joking around. This is important—it’s faculty politics.” I knew at that point that he and I were headed in different directions in our careers. Of course, the intervening 45 years have proved me completely correct.

A couple years later he dragged me to Washington. It turned out a group of criminologists had some serious methodological problems, and they recruited a half a dozen or so young statisticians to come to Washington for a month. The idea was to teach us criminology and then give us these knotty problems to work on.

Steve was one and I was another, and there was Kinley Larntz and Gary Koch and some others whose names would be familiar to you if I could remember them. I do remember asking Al Biderman, who was one of the organizers, “Why don’t you instead bring a bunch of criminologists and teach them statistics?” He said “Are you crazy? They can’t learn statistics in a month.”

I was reminded of this at the Joint Statistical Meetings (JSM) when Mark Hansen, a statistician at the Columbia Journalism School, was talking about teaching statistics to journalism students because of how crucial a deep understanding of our science is in modern journalism. I asked, “Wouldn’t it be easier to teach journalism to some statisticians?” He said no, that journalism students have other talents that are important. I think he underestimates what statisticians can do. I also think I went very far afield from your original question.

Sam Behseta: Among all the pieces you’ve written in the last 25 years for CHANCE, do you have your own greatest hits?

Howard Wainer: My goodness. I’m not sure. I’d have to look at all of them. How do you pick your favorite child? But I will say something else that is allied to this. I believe that writing this column for CHANCE was the single best thing I’ve done in my career—for my career. Because what it’s done is forced me to write something intelligible every three months. As you know, every few years I have collected 15 to 20 of these essays together, reorganized them, rewrote them a little bit, wrote some interstitial material, and published a book. This has worked for four or five books so far. I don’t think I would have done this without CHANCE.

It’s hard for me to contemplate a whole book all at once, but all I had to think about was one article. The structure changes after the fact about how things could have gone together if I just had the wit to see it. All I had to do was reorganize and reorder the columns until they formed a coherent sequence. So most of the pain of writing books is gone. It’s really very easy.

I’ve had a substantial run over the last few years (thanks primarily to my employer and my co-authors) where there’s been a book a year for the last four or five years. Each one has focused on a superficially different topic. Of course, there are many graphical things and there’s some history. Those have always appealed to me. The book that came out on evidence in education had many interesting things, and that had the piece on value-added models you mentioned. That essay formed one of the core elements of Uneducated Guesses.

So I don’t know if I have a favorite child. Each of them has its own appeal.

Sam Behseta: Meanwhile, you’ve been teaching constantly.

Howard Wainer: Well, I would say teaching intermittently. I’ve taught for the last 10 years—one course a year at Penn. I taught for a couple of years at Princeton. When I was actually in the academic business I was at Chicago and taught there full time.

But I view teaching the same way I view cooking. I enjoy cooking one meal a week. You can plan it, shop for it, work hard at it, put your heart into it, and then watch the enjoyment that other people take in your efforts. Twenty-one meals a week is not for me. I think then you’re just throwing the stuff on the table.

I’ve taught one course a year for the last 10 years. I’ve worked hard at it, and I’ve enjoyed it immensely. The time came this year to cut back and so I thanked Penn for putting up with me for all this time and stopped. They have a really wonderful department. I don’t know who ranks such things, but I certainly would put them in the highest tier. It’s a wonderful place.

I’m honored that they let me be a small part of the department for this past decade. But now the time has come to stop. There’s a separation of three generations now between the students and me. They don’t get my jokes or many of my references. The students that are starting there this fall were born in 1995; most of my sport jackets are older than that. I have had fully adult women in my classes who were too young to date my children. That signals it’s time to stop.

Sam Behseta: Have you ever tried to use your pieces in CHANCE in your classroom?

Howard Wainer: All the time. All the time.

Sam Behseta: And what kind of feedback?

Howard Wainer: It’s always been positive. In fact there are several pieces in CHANCE that were co-authored with students. There was a piece on exploratory data analysis that I did with a student named Danielle Vasilescu that was about overcrowding among nations. Then there was a piece that I did with another student, Grace Lee, about the choice of independent variables required by dating services. It began when students asked why some dating services asked to include the length of your forefinger. No one could figure out why, but some of the girls were saying that there was an old wives’ tale about the relationship between the length of the forefinger and the lengths of other body parts. Others suggested that men often lie about their height, and finger length might provide a check on it.

Perhaps the dating service was going to use the length of the forefinger (who’s going to lie about that?) as a proxy for height. So Grace went and gathered forefinger lengths and heights of a number of students, looked at the correlation and calculated the standard error. It turned out that finger length isn’t much of a predictor, because the standard error of the estimate of your height from your forefinger yields bounds of about plus or minus six inches, and that wasn’t helpful.

The papers co-authored with the students illustrate one of the lessons I’ve learned from my wife. I’ve learned many lessons, but this one in particular is to always have students as first author. Or more specifically, the most junior person should be the first author. This first came to a head more than 20 years ago when I was writing something up and Linda asked “Why are you making Steve the third author?” I said “Well, it was my idea, I got the data for him, he did the analysis I specified, and I wrote it up so I’m the first author.”

She said those aren’t good enough reasons. She said what good is one more article on my résumé going to do for me? For him it will be important. I tried to explain to her the rules of how publications work, but she would hear none of it. Linda can be a very stubborn woman when she wants to be. So at that point I started always putting the most junior author first. I guess it’s a version of academic Marxism—from each according to their ability, to each according to their need. Since that time, I have always made the students first author.

I’ve been delighted to discover that as they’ve progressed in their careers, many of them have adopted the same practice. So I’ve had an influence on something that turns out, I believe, to be important. Fortunately my name is Wainer, so I can just say it’s alphabetical. But about 10 years ago I wrote something with Sandy Zabell, and the force of habit was so ingrained I made him first author.

Sam Behseta: One thing I tell my students is that statisticians should be able to write well because that’s what they do. They analyze data and then they write about it. And it often comes as a surprise to them because their thought process is, “I’m in a quantitative field; I’m not in literature or anything like that.” What’s your thought on that?

Howard Wainer: It’s come as a surprise to me, too. When I applied to college, I eliminated all colleges that required an essay. So I ended up going to Rensselaer Polytechnic Institute. I hated writing because I wanted to do math. So the joy that I take in writing now astonishes me. But of course, I think one’s success as an academic is related to many variables. Horsepower, of course, focus, grit, energy, good taste in problems, and the need to tell stories. You have to tell stories.

You have to construct some kind of narrative around what it is that you’re doing. No one’s going to pay attention to your stories unless you’re interesting and grammatical. And of course, the more you write, the more practice you get, the better you are at this. When I was at Chicago, one of the advantages of being a faculty member was that you could sit in on any course you wanted. So when I learned there was a course on writing that Saul Bellow gave, I jumped at the opportunity to audit it.

One of the things I’ve observed about mathematicians is that most of us are open to criticisms of our mathematics, but not of our writing. The response is usually “Who are you to criticize my writing?” But taking this course with Bellow, the “Who are you?” is easy to answer. He has a Nobel Prize, a Pulitzer, National Book Awards, etc. So I would write things, and he would give them back to me with comments. I would pay attention.

He always referred to writing as his craft, never as his art. He explained that the reason he has had the success he had was that he worked hard at it. Of course, he also has some game. So I work hard at it. I write and rewrite. It’s fortunate that I enjoy reading my own work so much that I can reread the same thing several times and constantly pick away at it and to try and find le mot juste. So I emphasize the importance of clear writing to all of those with whom I work.

Reading broadly is sometimes daunting. For when you read something written by a real pro, the tendency is to throw up your hands in surrender and think, I could never do that. That may be true, so instead you have to focus on clarity. Style may come later, but clarity must come first. There are lots of wonderful statisticians who wrote terribly. How many papers/books begin with such catchy phrases as “Let I be an index set.” A long way from “Call me Ishmael.” John Tukey was perhaps the most influential statistician of his time, but no one would confuse his writing with Hemingway’s.

Sam Behseta: Did you know him personally?

Howard Wainer: Oh, yes.

Sam Behseta: How did you interact with him? How did that come about?

Howard Wainer: I was a student and he was a faculty member. And then I subsequently worked with him for about 30 years. When I was at ETS, I would see him frequently. He was a remarkably generous man. And so when I had an especially thorny problem, I would go and talk with him about it. He’d always have something valuable to offer.
One important lesson he taught to so many of us was about the evil of hubris. He said hubris is a bad thing because—he didn’t say this, but it was certainly implicit in what he was saying—because people are—the phrase he used was—“people are different.” And you don’t learn anything if you don’t both listen to other people and respect what they have to say. He was probably the best hubris destroyer you could imagine. You could go to talk with him about anything and quickly realize that you’re not as smart as you thought you were. John frequently understood your problem better than you did before you arrived, but if he didn’t, he certainly would before you left.

As nearly as I can tell, John knew pretty much everything. There’s a wonderful story about him that I believe comes from Colin Mallows. John would sometimes get annoyed because people would ask him ridiculous questions just to see if he knew the answer. So Colin suggested, if you want to find out how to milk an elephant, don’t ask John directly. Just come in and start talking about elephants in general and eventually when the conversation got around to how to milk one, he’ll tell you.

Sam Behseta: Was he a good teacher?

Howard Wainer: It depends on how broadly you define teacher. In the deepest sense, he was wonderful; as a lecturer, less so. He was very generous, and he was so full of ideas. The key thing you had to learn is if he made a suggestion that seemed, on its face, to be completely ridiculous, it’s because you don’t understand. So you have to take him seriously under all circumstances. Later, when you learn more, you will realize that that’s what you should have been thinking about.

David Donoho tells the story of when John was visiting Stanford for some extended period of time. All he was interested in—this was maybe 20 years ago, and maybe longer than that, 25 years ago—all he was interested in were fast algorithms for processing text. Donoho thought to himself, “He’s lost it. Who cares about this?” Then, a decade later, Google grew to be a multibillion-dollar business. David said this taught him not to gainsay anything that Tukey is doing. And Donoho also has some game.

Sam Behseta: I’m sure. Right. Were you involved in the whole discussion between Bayesianism and frequentism, and did you take sides early on, or it didn’t matter to you?

Howard Wainer: No. I have gradually become what is pejoratively referred to as an opportunistic Bayesian. That’s if it works I will use it, and if it doesn’t I won’t. But certainly my training was never from a Bayesian perspective. In fact, only recently did I learn why John never seemed to be a fan of Bayesian methods. This attitude was a mystery to me. I couldn’t understand why Tukey wasn’t more positive toward Bayesian methods as they became more practical.

Recently the book The Theory That Wouldn’t Die, about Bayes’ Theorem, explained that Tukey was using Bayesian methods during the cold war period on matters of national security, but he chose not to make it public because it conferred a tactical advantage to the United States. I guess that carried over into the predictions of election outcomes for NBC as well.

But I think he probably, and this is just a surmise on my part—I don’t pretend to understand him—but I have a feeling that he was against dogmatism because it stood in the way of the flexibility required for a first-rate scientist. And many of the Bayesians of the period were certainly dogmatic. So he might have been in favor of Bayesian methods, but not Bayesians. If you remember statistics in the ’50s, it really was almost warfare. My friend Sam Savage tells how his 9-year-old brother was accosted at some Stanford faculty party by somebody who told him “Your father is deeply deluded.” He was talking to a 9-year-old and yet he felt it was important to point out the moral weaknesses of a Bayesian attitude.

Sam Behseta: I guess you had to identify yourself with a camp back then. And it didn’t make much sense because I think, at some juncture, it was an ideological warfare between the two camps.

Howard Wainer: Well, it’s almost over now. Because there are problems that you can’t solve any other way.

Sam Behseta: At this juncture, with the whole Big Data and the immediate interest in solving problems, which are at the core of science, it is possibly more efficient to be a pragmatist.

Howard Wainer: Yes. The Shibboleth is, “Does it work?” Fifteen years ago or so, I teamed up with a very young Eric Bradlow (who has since become an eminent professor at Penn). He was a dyed-in-the-wool-Bayesian, and he taught me a great deal. We did a book together that was a fully Bayesian approach to doing test theory, and it forced me to learn (and eventually love) the stuff. At the time, I wasn’t fully conversant with the estimation methods that made Bayesian procedures practical. MCMC [Markov chain Monte Carlo] became my favorite initials.

I was fortunate to be working with Eric for many reasons, but one of them is because he works the same way I do—that is, everything should be done by yesterday. When I was editing a journal, he was my chief associate editor. I would send him a manuscript, and he’d send me his review an hour later. I think the median response time for a rejection letter was under a day. Fast rejections are easy. But we also had one paper that we accepted within an hour. Now that’s much harder to do. And I had three reviews within an hour.

Sam Behseta: Wow.

Howard Wainer: Fully worked out and sent back. What happened was I got a very nice submission from Don Rubin. So I had a prior. I emailed it to Eric and to Andy Gelman. Andy also works blindingly fast. Within an hour, I had full reviews from both of them. I think Andy had actually seen an earlier version of the paper, so he had time to think about it. The reviews included broad comments, suggestions, and even spelling and grammar corrections. The paper came in at nine, and Don had the reviews back before lunch. That’s a record.

Sam Behseta: When I talk to PhD students, sometimes they think that every PhD in statistics, when they finish their education, they ought to go to academia. But my story is, listen, actually that’s a very small minority. A good chunk of PhD graduates will end up in other sectors and can be extraordinarily effective in shaping up policies. How rewarding has the experience of working outside academia been to you?

Howard Wainer: Well, I’ll tell you. I certainly came out of graduate school with the same point of view that you expressed—I was training to become a professor. For no good reason, other [than] who is doing the training—professors. It also seemed like a nice life. So that’s the direction I wanted to go.

I found, as I pursued this path, that there were aspects of being an academic that are very attractive—certainly the teaching part. The part that I didn’t like, because it was too difficult, at least for me, was discerning what were good problems to work on. When I first got out of graduate school, I went to Temple University and I was unhappy. I thought, well, that’s because Temple wasn’t Princeton. I left quickly and I got a job at the University of Chicago. Chicago is as good a university as you can find. Chicago treats its faculty as well as you can expect to be treated. It’s a wonderful place, and the colleagues I had were terrific. And still I wasn’t happy.

I found that there was an enormous amount of intellectual horsepower being used to solve trivial problems. That wasn’t everybody, obviously. There is certainly wonderful and important work that comes out of academics, but since I had yet to develop good taste in the choice of problems, the university life was ill suited to me—at least at that time. So I left Chicago and went to Washington. I worked there during the Carter administration. Toward the end of my time there I met with Don Rubin who was, at that point, preparing to go to work for the EPA [Environmental Protection Agency] and was assembling an all-star cast to go with him. He had already recruited Paul Rosenbaum and Rod Little, and it was flattering to be considered to be part of such a group. And Don had some interesting ideas.

But the more we spoke about it, the clearer it became that what I was interested in doing was essentially substituting (never replacing) for Don at the Educational Testing Service (ETS), which he was leaving to go to EPA. And so I went in that direction. I even rented Don’s Princeton house from him while he lived in Washington.

At ETS, we had real problems—real in the sense that if you solved one it could have important and immediate consequences. For example, there was one problem having to do with improving the efficiency of the shipping of tests to the hundreds of testing centers. You can’t send them too early or send too many extras because of security issues. You can’t send them too late because if they arrive late, that doesn’t help. How do you know how many to send? At the time, they had ad hoc rules in which on the last day that would allow normal shipping, they would send the number of tests equal to ten percent more than the number of people who had so far registered for that particular site. If more were needed, they would overnight them at the last minute. With hundreds of testing centers, this was expensive.

Paul Holland was looking at this problem and he said if X is the number who registered by shipping time last year and Y is the number that showed up eventually, we need a rule to predict Y from X. Statisticians know how to do this. It turned out that that regression equation saved ETS some number of hundreds of thousands of dollars every year.

That’s a nice reward for doing a regression. ETS, in their generosity, then gave the statistics group an annual grant of a fairly large number thousands of dollars a year to support whatever research we felt was worthwhile within our group. You can see how such work could be rewarding—and we actually did other things of broader interest.

Don Rubin did some work on estimating the validity of the Law School Admission Test at each law school separately—even those with smallish enrollments. That led to his important 1980 JASA [Journal of the American Statistical Association] paper on empirical Bayes methods. Paul Holland wrote his wonderful 1986 JASA paper on causal inference from his many experiences at ETS.

There were all sorts of important and interesting problems that appeared because ETS was a data-rich environment with a lot of resources. So I found it to be an advantage, at least for me. I still like a lot of things about being an academic, but my career certainly was better this way. When I was 58, I left ETS and went into the medical field, delighting my Jewish mother.

Moving into medicine has meant there were many new things to learn about. As one ages, it’s good to know more about medicine—and more about doctors. So it’s been useful and great fun. The mixture I’ve had of academics and industry is just about right for me. As I think back to my graduate school days, Tukey served as a guide star. He was full time at Bell Labs solving very hard practical problems and full time at Princeton and also full time in working for the federal government on one topic or another. It’s too hard to use him literally as a model—nobody can do that. But you can try to get as close as your talent and energy allows.

Sam Behseta: And I see that lapel on your jacket, it says 1746. What’s the story behind that?

Howard Wainer: Oh, that’s the date that Princeton was founded. If you promise to leave Princeton a certain amount of money in your will, you are given membership in the 1746 Club and you get a free button. An additional benefit, beside the button, is periodic invitations to activities suitable for aging Tigers.

Sam Behseta: Before we close, any word of wisdom for the younger generation of our readership?

Howard Wainer: Now you’ve got me. I guess it would be a variation of “follow your bliss.” I’ve always felt that if you do something you enjoy, do some more of it. I advise students in trying to pick a major that they do that. You’re a fool if you go to a great university with your mind all made up about what you’re going to study. You should let the university influence you.

I advise them to start with a broad range of courses, and if they find a course that they really like, take another course from the same instructor or on the same subject. And if you like that take the next one. Pretty soon it’s clear what your major is. The same procedure can be adapted for setting the direction of a career. It’s hard to know in advance what to do. So you do whatever seems to be rewarding, and if that keeps working, keep doing it.

The counter-example to this is Rubin. Don, when it came time to write his thesis, didn’t behave the way the rest of us did, which is to panic and pick up a topic that was lying on the ground nearby. Don actually went away for a few months and thought about it. He thought about what were the biggest problems facing statistics and came up with missing data. Remarkably, he made a dent in it.

In the intervening 45 years or so that he’s been working on that, his judgment has been proved right. It’s been an enormously rich thing. But most of us aren’t smart enough to see in the long term what’s going to be good. So we decide in the short term, which is fine—but only if you are willing to reverse field.

Last, it is crucial to remember that as soon as you stop learning new things, your career is over. The easiest way to keep learning new things is to collaborate, especially with younger people. I’ve been enormously fortunate in working with some terrific young people. As an example, when Ken Shirley was my TA [teaching assistant] at Penn, he asked incredulously, “You don’t use R?” I immediately learned it—an effort that has paid for itself dozens of times over in the interim. Working with younger scholars has stretched me in important ways and forced me to learn new things. Of course, my work has been improved because of it. My experience of the value of collaboration resonates with George S. Kaufman’s observation that collaboration is “Gelt by association.” Indeed!

I fear at one point or another in one’s career it becomes easy to start to coast. But if you do that you have to be willing to say that’s the end. That what you know now is all you’re going to know. That’s very hard to do.

It’s akin to something financial advisors observe when people retire. They find that the hardest thing for retirees to do is to start spending principal, for in order to spend principal you have to acknowledge, deep down in your core, that you’re going to die—and in some relatively finite amount of time. Even if you calculate that the principal will last until you’re 104, it doesn’t matter. Whatever year you pick, when principal starts to go, you’re acknowledging your own mortality.

The principal that we work with is what we know and can do. When we stop adding to that, we’re acknowledging our own mortality. I think it’s hard to do that, and so it’s sensible to keep putting stuff away. My grandparents, for all their lives, always saved 10 percent of their income—always. At age 80, they were still putting 10 percent away. At the time I thought it was idiotic, but now I understand what that means, and so I keep adding.

Sam Behseta: Howard, it was a pleasure. And it’s been a pleasure working with you.

Howard Wainer: Sam, I’ve been honored by this. I’ve had a wonderful time, and it’s not over.

Sam Behseta: Thank you so much.

Howard Wainer: Thank you, Sam.

About the Author

Sam Behseta earned his PhD from Carnegie Mellon University and is a professor at California State University, Fullerton. His main research area is statistics in neuroscience. Other research interests include stochastic modeling of decisionmaking with multi-alternatives, Bayesian functional data analysis, Bayesian nonparametrics, statistical modeling of epidemiological data, and probabilistic watermarking. Behseta has trained and mentored undergraduate and graduate students.

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