Mollie Orshansky - Amstat News - American Statistical Association

SPECIAL CAREER ISSUE • SPECIAL CAREER ISSUE • SPECIAL CAREER ISSUE 

Publications Agreement No. 41544521 

September 2008 • Issue #375 

The Membership Magazine of the American Statistical Association 

Making a Name: 

Up-and-Coming Statisticians 

on the Verge of Great Things 

Statisticians in History

VISION STATEMENT 

To be a world leader in promoting statistical 

practice, applications, and research; publishing 

statistical journals; improving statistical education; 

and advancing the statistics profession. 

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Special Contributors 

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MISSION STATEMENT 

Support excellence in statistical practice, research, 

journals, and meetings. Work for the improvement 

of statistical education at all levels. Promote the 

proper application of statistics. Anticipate and 

meet the needs of our members. Use our discipline 

to enhance human welfare. Seek opportunities to 

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CAREER 

Read about the careers and 

lives of statisticians whose 

names are recognizable 

as both published 

authors and scientists 

DAY IN THE LIFE 

21 Life as a Stochastic Modeler 

Alan Gelfand, Duke University 

SPECIAL MATERIAL 


STATISTICIANS IN HISTORY 

4 Kimiko Bowman 

Never Say Never 

7 Irene Hess 

Sampling Is in the Details 

11 Jack Chao-sheng Lee 

13 Paul Dixon Minton 

Building a Department 

25 On Becoming a Teacher 

Daren Starnes, The Lawrenceville School 

Statisticians and other experts 

offer career advice for 

those looking to sharpen 

their quantitative careers 

15 Mollie Orshansky 

Author of the Poverty Thresholds 

30 What Does a Teaching Associate Professor Do? 

Pam Arroway, North Carolina State University 

28 Where Are Your Colleagues? 

38 Top Five Favorites 

CAREER GUIDE 

34 Snakes and Ladders: Building a 

Career in Statistics 

David L. Banks, Duke University 

54 Math Is Music; Statistics Is Literature 

(Or, Why Are There No Six-Year-Old Novelists?) 

Cover design by Megan Murphy 

HISTORY 

43 Are You Media Shy…or Media Savvy 

Rosanne Desmone, 

ASA Public Relations Specialist 

46 Making a Name: 

Up-and-Coming Statisticians on the 

Verge of Great Things 

DEPARTMENTS 

1 President's Invited Column 

61 Professional Opportunities

PRESIDENT’S INVITED COLUMN 

Statistics in Monitoring the Nation’s Health 

Nathaniel 

Schenker 

This month, I have invited Nathaniel Schenker of the National Center for Health Statistics to discuss how that 

agency provides information about the nation’s health. Since before 1960, NCHS has monitored health statistics, 

initially under the stewardship of Forrest Linder. It is a wonderful resource for data and information. 

2 AMSTAT NEWS SEPTEMBER 2008 

I’ve spent the past nine years at the 

National Center for Health 

Statistics (NCHS), having previously 

taught biostatistics at UCLA for 

11 years and worked at the U.S. 

Census Bureau for three years before 

that. NCHS, which is part of the 

Centers for Disease Control and 

Prevention, is the nation’s principal 

health statistics agency. Its motto is 

“Monitoring the Nation’s Health,” 

and its mission is to provide statistical 

information that will guide actions 

and policies to improve the health of 

the American people. 

What Is Done at NCHS? 

When I used to take my infant son 

for a medical check-up, the pediatrician 

would plot his height and 

weight on growth charts. Little did 

I know the charts were produced by 

NCHS and were based primarily on 

data from NCHS’ National Health 

and Nutrition Examination Survey 

(NHANES). Similarly, when cars 

began to use unleaded gasoline, I 

didn’t realize the formulation of policy 

to move away from leaded fuel was 

based on analyses of data on bloodlead 

levels from the NHANES. (For 

more on this policy, see the President’s 

Invited Column in the May issue of 

Amstat News). 

Indeed, NCHS collects and analyzes 

data and produces information 

for the public on a wide range of 

health topics, including vital events 

and demography, health conditions 

and behaviors, access to and quality 

of health care, and injuries and 

disabilities. Sources of NCHS data 

include birth and death certificates, 

as well as various sample surveys that 

collect information via review of 

medical records, personal interviews, 

physical and dental examinations, 

and laboratory tests. 

‘S’ Stands for ‘Statistics’ 

Given that the “s” in NCHS stands for 

“statistics,” statisticians obviously have 

a major role in the work of the agency. 

As do many agencies in the federal 

statistical system, NCHS has separate 

divisions that focus on specific data collection 

systems and topics. Statisticians 

within these divisions work on the 

design and implementation of the 

data collections, analyses of the data, 

and preparation of the data for release 

to the public. Other statisticians (such 

as I) work in offices within the agency 

that focus more on cross-cutting analyses 

and research and on the development 

of statistical methodology, while 

also providing consultation to the data 

divisions. Examples of methodological 

research areas at NCHS include 

optimal strategies for sample design, 

questionnaire design, modeling and 

estimation, data mining, developing 

indexes of health, confidentiality, and 

handling missing data. 

Nathaniel Schenker, Senior Scientist for 

Research and Methodology, NCHS/CDC 

An NCHS statistician often works 

as part of a team, together with other 

statisticians as well as specialists 

in areas such as computer science, 

demography, economics, epidemiology, 

medicine, public health, and 

sociology. There are ample opportunities 

for individual research projects, 

as well. Moreover, collaborations frequently 

occur with researchers at other 

federal and local government agencies, 

in industry, and in academia. 

Mr. Academic Goes (Back) to 

Washington 

When I decided to leave my tenured 

academic job in UCLA's Department 

of Biostatistics in sunny California to 

return to the East Coast and work for 

the government, I was often asked, 

“Are you crazy? Why would you do 

that?” Well, there were a number of 

reasons, some of them personal, such 

as my growing up in Washington and 

the East Coast having a lot to offer. 

As a statistician, however, I was excited 

by the prospect of working at the 

source of health data that are important 

and of interest to many outside 

the agency. Indeed, at UCLA, I often 

ran across NCHS data and researchers 

analyzing them. Also, NCHS seemed 

like a relatively small, friendly agency, 

so I didn’t feel as though I’d get lost 

working there. And, it seemed to have 

a culture of conducting research, so 

I thought I’d be able to continue my

esearch career. (After coming to NCHS, 

I also became an adjunct professor in the 

Joint Program in Survey Methodology at 

the University of Maryland, so I didn’t have 

to abandon academia completely.) Finally, 

the Washington area has an exceptionally 

active statistical community. For example, 

it is home to the largest chapter of the ASA: 

the Washington Statistical Society. 

Do I miss being a full-time professor at a 

fine university? Yes. Am I glad that I changed 

jobs? Absolutely. Besides the fact that 

changing jobs every once in a while helps to 

'keep one’s blood flowing,' I’ve enjoyed the 

atmosphere and work at NCHS. I’m less of 

a free agent in some ways now than I was 

in academia (although I also don’t have to 

hustle for grant support the way I did), and 

there are some bureaucratic hassles involved 

in working for the government; but every 

job has its own administrative headaches, 

and any annoyances due to governmental 

red tape have been far outweighed by the 

opportunities I’ve had for interesting work. 

A Small Sample of My Projects 

To illustrate some of the statistical work 

that goes on at NCHS, I’ll briefly outline 

a couple of major projects in which I’ve 

been involved. 

In 1997, the Office of Management 

and Budget revised the standards for classifying 

federal data on race and ethnicity. 

A key provision was that respondents to 

federal data collections be given the option 

of choosing more than one race group to 

describe the person in question. Because the 

previous (1977) standards called for only a 

single race group to be reported, data collected 

under the 1977 standards are not 

comparable with data collected under the 

revised standards. This can cause problems 

in trend analysis, the calculation of a vital 

rate—for which the number of events in 

the numerator and the population size in 

the denominator can come from different 

sources using different standards—and in 

other types of studies that combine data 

classified under the two standards. 

The decennial census, a widely used 

source of denominators for vital rates, began 

allowing multiple-race reporting in 2000. To 

make the 2000 census data (and intercensal 

and postcensal estimates) comparable 

with data classified according to the 1977 

standards, NCHS, with assistance from the 

U.S. Census Bureau, produced “bridged” 

census counts by county, age, sex, and 

race—estimates of the counts that would 

have been obtained had the prior standards 

been in effect (www.cdc.gov/nchs/about/ 

major/dvs/popbridge/popbridge.htm). The 

bridging process used models predicting 

responses under the prior standards from 

responses under the current standards and 

covariates. These bridging models were 

developed using data from NCHS’ National 

Health Interview Survey (NHIS), which has 

allowed multiple-race responses for many 

years, but which also asks multiple-race 

reporters for a single race group that best 

describes the person in question. 

I worked on many aspects of this project, 

but perhaps my biggest contributions were 

to the formulation of the overall approach 

used to solve the problem, the development 

of the bridging models, and the derivation 

of methods for assessing uncertainty in analyses 

that use the bridged data. 

A second project involved multiple 

imputation of missing income data. The 

NHIS provides a rich source of data for 

studying relationships between income 

and health and for monitoring health 

and health care for persons at different 

income levels. However, the nonresponse 

rates are high (roughly 30%) for two key 

items: total family income in the previous 

calendar year and personal earnings from 

employment in the previous calendar year. 

To handle this missing-data problem and 

allow analysts of the data to assess the 

uncertainty due to missing data, multiple 

imputation of these items has been 

performed for several years (www.cdc.gov/ 

nchs/about/major/nhis/2006imputedincome. 

htm) in collaboration with researchers at 

the University of Michigan. 

Several features of the data made the 

project particularly challenging and interesting. 

First, the data are hierarchical 

in nature, with one of the key variables 

reported at the family level and the other 

reported at the person level. Second, there 

were cases in which the value of one variable 

(e.g., personal earnings) could be 

restricted by the value of another variable 

(e.g., whether the person was employed), 

but the values of both variables were missing 

simultaneously. Third, in some cases, 

family income and personal earnings needed 

to be imputed within bounds because 

partial information was available about 

them (e.g., when a range was provided for 

family income, rather than an exact dollar 

value). Finally, several variables of various 

types (continuous, categorical, count) were 

used as predictors, but they sometimes had 

small amounts of missingness as well. 

I was involved in many aspects of this 

project (as with the bridging project), but 

especially in developing and evaluating the 

methods for imputation, writing technical 

documentation, and consulting with analysts 

using the multiply imputed data. 

An Exciting, yet Sometimes 

‘Scary,’ Job 

The two projects just described have several 

general characteristics in common. First, 

they required teamwork, both among staff 

at NCHS and between NCHS and other 

organizations. Second, they required statistical 

research. In fact, both projects led 

to publications in JASA [98(464) and 

101(475)]. Third, the problems were 

complicated, and they required making 

assumptions and implementing approximations 

for their solution. Finally, there 

was great interest in the data, and people 

both inside and outside the agency were 

clamoring for release of the data while the 

work was going on. 

Projects with the second through fourth 

characteristics create the need to “push the 

envelope” with regard to methodology 

under time constraints while maintaining 

the integrity of the data. This results in a 

job that is exciting, yet sometimes ‘scary.’ 

Challenges and Opportunities 

Statistical work at NCHS, and in the 

government in general, is challenging 

for administrative and technical reasons. 

For example, budgets are tight; costs are 

increasing for data collection, processing, 

and dissemination; there is a desire on the 

part of policymakers, researchers, and others 

for more information; and the protection 

of confidentiality is becoming more 

difficult. Oh, yes, and let’s not forget the 

famous “graying of the federal work force,” 

with many employees nearing retirement 

age. 

Of course, with challenges come 

opportunities. Difficulties due to sparse 

resources and conflicting desires and constraints 

with regard to data will need to be 

addressed through the creative development 

of efficient methods for collecting 

new data and methods for getting the most 

out of analyses of existing data. And the 

‘graying’ work force will result in openings 

for new generations of statisticians to carry 

out such work. ■ 

SEPTEMBER 2008 AMSTAT NEWS 3



Kimiko Bowman 

Never Say Never 

Bowman Helps Provide Formula for Approximating the 

Distribution of Maximum Likelihood Estimators 

Val Snider 

ASA Publications Coordinator 

She could have been a horticulturist. She 

had, after all, thought it would be exciting 

to create new plants. But Kimiko Bowman, 

better known by her byline as K. O. Bowman, 

chose to be a statistician and help develop a formula 

for approximating the distribution of maximum 

likelihood estimators. 

It was by chance, really, that Bowman chose 

statistics. Some might even say statistics chose 

her. She began her college education majoring in 

home economics at Radford College. “I wanted 

to make sure I would graduate, and I thought 

home economics would be relatively easy,” said 

Bowman. “In my second year, the president of 

the college called me into his office and advised 

me to change my major to the science field. He 

thought my future would be much better in science. 

I [had] liked mathematics ever since I was a 

small child, so I immediately changed my major 

to math and chemistry.”

Kimiko Bowman, who became a U.S. citizen in 1958, 

was the first ‘foreigner’ to receive a doctorate from the 

University of Tokyo. 

About a year later, Bowman graduated with a 

bachelor’s degree and won a National Institutes 

of Health fellowship in mathematical statistics 

from Virginia Tech that sealed her interest in the 

statistics profession. Three years after that, she 

had in hand a master’s degree and PhD in statistics. 

She completed her education with a doctorate 

in mathematical engineering from Tokyo 

University, but it was during her time at Virginia 

Tech that her research really took hold. 

Bowman met L. R. Shenton, her thesis 

advisor, at Virginia Tech and—for the next 

45 years—worked with him to provide a formula 

for approximating the distribution of 

maximum likelihood estimators. They succeeded 

by developing formulae for skewness 

and kurtosis statistics of maximum likelihood 

estimators with respect to sample size and by 

developing an approximation formula for the 

percentage points of Pearson distributions. 

By combining the two results, Bowman and 

Shenton were able to approximate the distribution 

of maximum likelihood estimators. 

Another important accomplishment was 

successfully implementing a divergent series 

algorithm for large computers. “In sampling, 

statistics series like this often occur, and the 

algorithm for the coefficients is extremely 

complicated and reaches four-dimensional 

At Virginia Tech, Kimiko Bowman worked with L. R. Shenton, her thesis advisor, to provide a 

formula for approximating the distribution of maximum likelihood estimators. 

space,” said Shenton. Bowman not only implemented 

the algorithm, but “it works, thanks 

to Dr. Bowman,” said Shenton. 

Bowman’s life wasn’t all work, however. She also 

found time to be a strong advocate for those with 

disabilities. She served on the National Science 

Foundation Equal Opportunities in Science and 

Engineering advisory committee and Committee 

on People with Disabilities. She wrote a report 

while chairing the latter that resulted in grants 

being set aside to provide accommodation or 

special equipment for people with disabilities— 

which helped promote their participation in science 

and engineering at NSF. 

Bowman also chaired the Statistical Tracking of 

Employment of People with Disabilities task force 

for the President’s Committee on Employment of 

People with Disabilities. Her main duty was to 

monitor the questionnaire for Census 2000 so 

relevant questions were asked about people with 

disabilities and accurate results were obtained. 

Perhaps her interest in being a champion for 

people with disabilities stems from the physical 

hardships she, herself, has overcome. In her youth, 

Bowman suffered from polio that paralyzed 

her from the neck down. Doctors told her she 

would never walk again, but after two years of 

“ One must not 

lose sight of 

what we want 

to accomplish. 

So, I tried 

not to worry 

about slights 

or insignificant 

things. 

” 



rehabilitation, she was walking. Gladys Reynolds, 

a statistician and good friend of Bowman’s from 

her Virginia Tech years, said, “I want to highlight 

her determination and perseverance. … In 1980, 

she was having polio-like symptoms and was 

diagnosed with Post Polio Syndrome. In spite 

of this, she has continued to be as active and 

proliferate as always.” 

Bowman considers herself a “triple minority,” 

being an Asian and a woman, as well as someone 

who suffers the effects of polio. “There were 

many struggles I had to overcome,” Bowman 

said. “However, one must not lose sight of what 

we want to accomplish. So, I tried not to worry 

about slights or insignificant things.” 

Instead, she became one of the first women 

to be elected Fellow of the American Statistical 

Association in 1976, worked hard to advance scientific 

research using statistics, advised a number of 

graduate students, and published three books and 

approximately 200 papers. One of those papers, 

“Tables for Determining Statistical Significance 

of Mutation Frequencies,” coauthored with M. A. 

Kastenbaum, received a Citation Classic in 1989 

for being the fourth most-cited paper in the history 

of Mutation Research, an international journal. 

Additionally, Bowman is an elected fellow of 

the American Association for the Advancement 

of Science (1970), an elected member of the 

International Statistical Institute (1978), and an 

elected fellow of the Institute of Mathematical 

Statistics (1987). 

Kimiko Bowman, 

at Virginia Tech, 

advised a number 

of graduate 

students 

and published 

three books and 

approximately 

200 papers. 

In an effort to do more for minorities in 

the statistics profession, Bowman also became 

a contributing editor to the Current Index to 

Statistics in 1977 and continued in this position 

for more than 10 years. During that time, she 

translated articles from Japanese journals into 

English, added keywords and abstracts, and 

included them in the index. She also was asked 

to go to Japan as a liaison scientist for the Office 

of Naval Research. She initially went for three 

months, but continued to visit throughout her 

career. In 1987, she was invited to attend the 

International Statistical Institute meeting in 

Tokyo and to give a keynote address at the satellite 

meeting at Mt. Fujiyama. During the meetings, 

she received an audience with the Crown Prince 

and Princess of Japan—an unforgettable memory 

for her. 

In 1994, Bowman retired from Oak Ridge 

National Laboratory as a senior research scientist. 

Retirement has not slowed her down, 

however. She continues to be a guest scientist 

in the Computational Sciences and Engineering 

Division at Oak Ridge, working on research in 

distributional properties of estimators and test 

statistics under non-normal sampling. She also 

consults with colleagues within Oak Ridge on 

aspects of statistics such as procedures for validating 

computer models in economics and stochastic 

models in epidemiology and biology. In the 

words of Reynolds, “As a woman, minority, and 

a person with a disability, she has certainly been 

an inspiration and mentor to many of us in the 

scientific community.” ■

Interview with 

Irene Hess 

Sampling Is in 

the Details 

Fritz Scheuren 


Ida Irene Hess was born in Muhlenberg County, Kentucky. Her father was a mining engineer in Central 

City. Her mother, who graduated from Valparaiso College with her father, worked at home. After 

graduating from Indiana University, Hess returned to Central City to teach math and English at the 

local junior high school from 1932 to 1942. In 1940, she came to work with Leslie Kish at the Survey 

Research Center. Under Kish, she trained many graduate students in the details of sampling. The following 

is an interview with Hess, conducted in February 2008 by Fritz Scheuren, an ASA past-president. 

[Scheuren] Irene, it is so good to see you well and still active professionally 

at 97. Thanks for letting me interview you for all your 

many friends and colleagues who want to catch up with all you 

have done. Can we start with something about your family and 

early life before you came to the University of Michigan? 

[Hess] I grew up in Kentucky. In addition to my parents, I had 

one sister, Beulah Marie. I was at Evansville College three years 

and then went to Indiana University for one year for a bachelor’s 

degree, but that’s the only degree I have. I guess you want me to 

talk about my start as a sampler? 

[Scheuren] Well, of course, but a little more please about what 

you did before that. Didn’t you teach in high school? 

[Hess] No, I taught in junior high school in Central City, 

Kentucky. I was really not happy doing something like that, 

though. I was always interested in mathematics, and I would have 

liked teaching mathematics at a higher level. And, when you are 

teaching in public school, you first know that you’re to teach children. 

And I really didn’t enjoy that. I couldn’t enjoy grade-school 



mathematics. I did not want to be responsible for teaching children 

and encouraging them in what I would consider to be the 

‘right way.’ 

So, I decided to try for statistical employment in the federal 

government. But to pass the civil service exam, you had to have 

a minimum of six hours in statistics and I did not have that, so I 

went about getting it. I considered Indiana first, but there wasn’t 

anything there that interested me at that time. I was aware of Iowa. 

Leslie [Kish] asked me once why I didn’t go to Iowa. I didn’t tell him 

why, but my mother’s parents lived in Iowa. It was always so hot 

in Iowa in the summer, so I just wouldn’t consider going to school 

in Iowa because I just couldn’t take that heat. I ordered catalogues 

from several universities—Indiana, Michigan, and Kentucky, perhaps 

others. I came to Michigan two summers (1940, 1941). That 

is how I got my six hours in statistics, and that was what I needed 

for the civil service exam, which I passed. 

[Scheuren] And you came to Washington after that? 

[Hess] Yes, it was right after the start of World War II in December 

and there was a civil service exam in May. The Bureau of Labor 

Statistics sent out invitations, and that’s what I applied for and 

went first to the Bureau of Labor Statistics. Then, I was at the 

Bureau of Standards for a short time when they were developing 

the proximity fuse for bombs in the war. When the war was over, I 

decided I better get away from the Bureau of Standards. Of course, 

I had already been here, at Michigan, for two years because I was 

really interested in statistics. So, I went out to the [U.S.] Census 

Bureau and talked to somebody out there. They were just organizing 

the first sampling section at the [U.S.] Census Bureau. 

Anyway, I joined the sampling section at the [U.S.] Census 

Bureau around 1944 or something like that. I was there until 

I came here in 1954. Kish was in charge of sampling here in 


Irene Hess (left and above) worked at the U.S. Census Bureau during the late ‘50s and stayed 

there until her retirement in 1981. 

Michigan, but his assistant was going to get married and move 

away and Kish needed somebody else. I was recommended to 

Kish and he wrote to me. In the meantime, at the [U.S.] Census 

Bureau, I think at least twice, when I came to work in the morning, 

they told me I should leave. I had no retention points to 

justify my continuing employment at the [U.S.] Census Bureau. 

(When a person returned from military service, that person had 

to be re-employed.) 

[Scheuren] They were laying off people? 

[Hess] Yes, that was because of the return of veterans from the 

war, and every time someone came back and had to have a job, 

they selected me to lay off. That happened twice. Joe Steinberg 

worked hard to get me back each time. Anyway, on one Good 

Friday evening, I walked into my apartment and picked up my 

mail and there was this communication from Leslie Kish out here 

in Michigan. 

When I got the letter, I felt it was going to be a job offer and 

I knew that I was going to take it, but I didn’t want to. I liked 

Washington and I always liked the [U.S.] Census Bureau. I 

responded to Leslie’s letter, and he invited me here to look around. 

I’ve been here ever since, and that was 1954. In 1981, I retired. I’ve 

been retired for 27 years. 

[Scheuren] But you’ve been working here as a retired person, 

every weekday ever since? That’s a real compliment to you 

and to the center. 

[Hess] Well it has been a long time. The way it began was I had 

started this book [on sampling]. We had some very complex sample 

designs for various projects and it bothered me that nobody else 

around here knew anything about the sampling activities. So, I had 

written or was working on that book, Sampling for Social Survey 

Research Surveys, 1947–1980. I decided to stay and finish it. 

[Scheuren] So, that is how you are continuing to work after retirement? 

What came next that kept you coming here every day? 

[Hess] Roe Goodman. I don’t know if you were ever acquainted 

with Goodman. 

[Scheuren] No, I wasn’t, but I know of his work.

Irene Hess worked with Leslie Kish, but 

also worked with Mildred “Jean” Harter, 

seen here with Hess (above and right) in 

Hess’ office in the late ‘50 s. 

[Hess] Goodman was sold on controlled selection. 

[Scheuren] Well, I am too. In fact, one of my former students, 

Yan Liu, did her dissertation on balanced sampling with me—a 

related idea. 

[Hess] Goodman’s family was here in Michigan, so he came in one 

day concerned about how well-controlled selection by computer 

is compared with hand-drawn controlled selection. So, that’s how 

we happened to have the book Controlled Selection Continued. As 

I remember it, I think we were already well into the book before I 

had to retire. 

[Scheuren] You had to retire because of age? What was the age 

you had to retire? 

[Hess] Yes, I had to retire at 70. That was in 1981. 

Anyway, Goodman was very interested in controlled selection and 

the material we set up and worked on in the book. The last time 

I saw Goodman was one afternoon before Easter. Goodman’s wife 

was already in Kansas and he was going to go take the train and 

join her. Anyway, he put in his final edits to the book. We didn’t 

know they were final edits, of course, at the time. Well, he went 

home to Kansas. He had a heart attack and died that night. That 

made me determined to finish the book. 

When my time came to retire, I probably still had something 

to do there. I don’t know if it was all done—probably not. I don’t 

remember how long it took me to get these first two books finished 

after I retired. 

[Scheuren] But there were more books after that weren’t there? 

Leslie Kish, ASA president in 1977; 

he passed away in 2000. 

[Hess] Yes, several. The last one of these was published in 

September 2007, I think. 

[Hess, changing the subject] As you know, the center has done a lot 

of work on telephone surveys. But, I never got very deeply into the 

telephone field. All of these things I worked on were face-to-face 

area probability surveys. I believed wholeheartedly in this approach. 

And, hence, I never got involved in the telephone business. 

How it started at the center was Charlie Cannell and Bob Kahn 

got a project to work on telephone sampling. One day, I don’t 

know if it was Cannell or Kahn, asked me if there was somebody in 

the sampling section who could work with them on this telephone 

project they had and I told them Bob Groves. And, I also told Bob 

Groves that Cannell and Kahn would be talking to him. And they 

did, and they worked on various projects, and Groves has been 

with telephone surveys ever since. I was no admirer or interested in 

the telephone. Anyway, I was getting to the point where I had to 


Irene Hess (left) celebrates her 97th birthday with Rhea Kish on 

August 26, 2007. 

retire, so it was not something for me to get into and I had enough 

of these other things [talked about earlier] to finish up. 

[Scheuren] You sure did. Look at these accomplishments, all done 

after you retired, too. 

[Hess] Now, I’m completely out of any project and I don’t have 

anything that I really want to write about. So, I figure I ought to 

clean up everything that I have around here and get out. 

[Scheuren] Don’t do that. You do have more to do. At least help 

me edit this interview, please? 

[Hess] Bob Groves says he just likes to have me around. 

[Scheuren, changing the subject] Let me ask you about your work 

with Kish, and then we will finish up with your stint as the first 

chair of the Section on Survey Research Methods. 

Books by Irene Hess 

Probability Sampling of Hospitals and Patients (1961) 

Irene Hess, Donald C. Reidel, and Thomas Fitzpatrick 

Sampling for Social Research Surveys (1995) Irene Hess 

Controlled Selection Continued (2002) Irene Hess and 

Steven G. Heeringa 

The Practice of Survey Research at the Survey Research Center, 

1947–1980 (1985) Irene Hess 


[Hess] Early on, I did publish a number of papers with Kish. I 

remember a paper in The American Statistician on nonresponse. 

That paper was something I guess Kish shared with me, actually. 

That was just shortly after I came here. 

[Scheuren] That nonresponse paper that you and Kish did is a 

wonderful piece of applied work. I have cited it many times. When 

I used to do the History Corner in The American Statistician, I 

republished it. 

[Hess] After about 1960, Kish got out of the day-to-day sampling 

activities. He was going to educate the world. So, we didn’t work 

together much after that. For example, he did not have anything to 

do with the projects that I was working on and what we were doing 

with Roe Goodman. Kish was not involved with any of that. He 

was just in a separate area of the center completely. 

[Scheuren] One last item? Can you talk about your involvement 

with the Section on Survey Research Methods? 

[Hess] Do you know how the section started? Within the Social 

Statistics Section, there was a subsection, and I guess it existed for 

maybe two or three years. But anyway, at one time, I was nominated 

for chair of the subsection. There was an election within the 

Social Statistics Section and I happened to have been nominated 

and then elected for chair of the subsection. In the meantime, 

the powers that be decided to have a separate section for survey 

research, and they said—because I had just won the election— 

that I would automatically be chair of the new Section on Survey 

Research Methods. So, I was the first chair of the Section on Survey 

Research Methods, 1977. 

Do you remember this [showing the engraved silver plate given 

to her by the Section on Survey Research Methods in 1998]? 

[Scheuren] You still have this? I remember it well. I think I may 

even have given it to you. 

[Hess] That was my last annual meeting. I remember it very well. 

I was surprised, overwhelmed. 

[Scheuren] Irene, you have done such wonderful things for us all. 

I’m going to read this inscription. The inscription says, “Irene Hess 

for distinguished service and unstinting efforts in the furtherance 

of survey research methods.” 

[Hess] In July 1999, I had extensive surgery on my right leg, and 

I have never been free of some kind of health struggle since that 

time. And I have never been to another meeting of the American 

Statistical Association, and I guess I’ll never get there again. But 

anyway, 1998 was very special. That was a real graduation for me. 

[Scheuren] Oh my, you’ve done so many things, Irene, and most 

of your major publications were finished after you retired. Earlier, 

you were so busy day-to-day that you didn’t have time to write 

these books. But, you did write them, eventually. How can we all 

thank you? ■

Jack Chao-sheng Lee 

The following excerpt is based on the 2007 article “In Memory 

of Professor Jack Chao-sheng Lee (1941–2007),” which 

appeared in the Journal of Data Science, Vol. 5, 143–150. For 

a complete version, visit www.sinica.edu.tw/~jds/In-memory-of- 

Jack-Lee.pdf. 

Chao-sheng Lee can be described as one of the most outstanding 

people in Taiwan’s present statistical community. 

His 40-year career of teaching and research has resulted in 

nearly 100 academic papers in well-known international periodicals. 

The National Chiao Tung University (NCTU) Graduate 

Institute of Statistics, where Lee established himself when he 

returned from the United States, has become a cornerstone for the 

development of Taiwan’s growing statistical academic circles. The 

NCTU Graduate Institute of Finance, which Lee helped establish, 

has provided Taiwan’s financial sector with the talent needed 

for growth and innovation. 

Lee graduated from National Taiwan University’s Department 

of Business in 1964 and received his master’s in economics from 

the University of Rochester in 1969. He studied under Seymour 

Geisser at The State University of New York, Buffalo, and received 

his PhD in statistics in 1972. Upon completing his education, he 

began his teaching career at the University of Minnesota, and then 

taught at Wright State University, as well as other universities, for 

many years. He then went to Bell Labs and devoted himself to 

research in economics and statistics. 


Prepared by the 

Institute of Statistics, 

National Chiao Tung University 


In 1992, Lee was invited by Chi-Fu Den, the president of 

NCTU, and Tsang-Hai Kuo, the dean of the College of Science, 

to return to Taiwan and take up the post of chair of the newly 

established Institute of Statistics. Until stepping down from this 

post in 1999, he persuaded many top statistical researchers who 

had graduated from well-known schools to return to Taiwan 

and teach. He also encouraged the now common practice of 

research and acquiring software, as well as hardware. His efforts 

enabled the Institute of Statistics at NCTU to quickly rise 

to the level of other established institutes of statistics, namely 

those of National Tsing-Hua University and National 

Central University. 

In August 1995, Lee was the first academic in Taiwan to 

receive the ASA Fellow honor from the American Statistical 

Association. Lee had already been selected as a member of the 

International Statistical Institute in 1990 and elected as the 

president of the International Chinese Statistical Association in 

1992. He was invited on multiple occasions to make speeches 

around the world in many renowned schools, including 

Harvard, Columbia, Cornell, and [the] University of Minnesota 

in the United States; Oxford and [the] University of London in 

England; Hitotsubashi University and Kobe University in Japan; 

[the] University of British Columbia in Canada; and INSEAD 

in France, to name a few. 


In order to adapt to the trend of global financial development, 

Lee participated in the establishment of the Institute of Finance 

at NCTU in 2002 and took on the responsibility of head of the 

institute for two years. During his tenure in charge, he was elected 

University Chair Professor of NCTU in recognition of his dedication 

and accomplishments, as well as his reputation within academic 

circles. 

Lee had published many papers, and his research subjects 

included many fields in statistics, including multivariate analysis, 

time series analysis, growth curves, Bayesian inference, and classification 

and pattern recognition. He also published in other areas, 

such as portfolio management and option evaluation, with particular 

application to finance. 

Lee conducted his academic studies conscientiously. Since his 

return to Taiwan, he has supervised numerous exceptional master’s 

and PhD graduates. He has four PhD graduates who are currently 

teaching in well-known universities and colleges. His master’s 

graduates have also held pivotal positions in numerous areas of 

business and academia. His pupils owe much to him, and have the 

capability to build on his success and to further glorify what Lee 

has started. 

Throughout his life, Lee lived plainly and simply, and his passion 

for learning never diminished. He, now and always, will be a 

great role model for scholars to admire and learn from. ■

Paul Dixon Minton 

Building a 

Department 

Dwight B. Brock, 

Westat 

Paul Dixon Minton was born in Dallas, 

Texas, the third of four sons in the family 

of William M. and Addie Evelyn Croft 

Minton. Having grown up in the economically 

difficult times of the Great Depression, he was 

able to attend college only because he received an 

“emergency scholarship” to attend Southern 

Methodist University (SMU). The university was 

experiencing its own difficulties, struggling to fill 

classes, and decided to offer such scholarships to 

young Dallasites who had done well in high 

school, were recommended by their principals, 

and could not otherwise afford to go to school. 

Minton earned a bachelor’s and master’s 

degree from SMU, a school to which he would 

return later to found and direct a department of 

statistics. His studies were interrupted by World 

War II, during which time he worked as a cryptanalyst 

for the FBI. Following the war, he returned 

to Dallas as an instructor and graduate student 


at SMU in the Department of Mathematics. It 

was at that point that Minton was introduced to 

probability and statistics by Edwin Mouzon, who 

had, himself, written a dissertation on statistics 

at the University of Illinois. After completing 

his master’s degree, Minton was encouraged by 

Mouzon to continue his graduate studies in the 

new program in statistics at The University of 

North Carolina. 

Minton earned a PhD in statistics from 

North Carolina State University under the 

tutelage of Gertrude Cox, during the time the 

Institute of Statistics of the Greater University 

of North Carolina consisted of the Department 

of Mathematical Statistics at UNC Chapel Hill 

and the Department of Experimental Statistics 

at NC State in Raleigh. Many famous statisticians 

were either faculty members or visitors 

when Minton was a student there, including 

R. A. Fisher, Harold Hotelling, William Cochran, 


R. A. Fisher 

Harold Hotelling 

Jacob Wolfowitz 

William Cochran 


Jacob Wolfowitz, Herbert Ellis Robbins, Richard 

L. Anderson, Raj Chandra Bose, and others too 

numerous to mention. 

Minton once related a story in which Fisher was 

sitting in the front row of the audience when he 

(Minton) gave his first paper as a graduate student. 

As if that fact alone was not enough to frighten 

an already nervous graduate student, the situation 

became “infinitely worse” (Minton’s words) 

when Fisher left the room in the middle of the talk. 

Minton told another story about his final PhD oral 

examination, in which he had a bad case of laryngitis 

that he attributed to nerves. To his knowledge, 

he had the world’s only silent orals. 

Minton returned once again to SMU and 

began to build a set of courses in mathematical and 

applied statistics for students from a wide range of 

subject-matter departments in the university. These 

offerings gradually evolved into the formation of 

the Department of Statistics at SMU, now known 

as the Department of Statistical Science. At the 

same time, Minton began to recognize the importance 

of computing in statistics, and, as a result of 

his expressed opinions, he was assigned to direct the 

first computer center at SMU, which housed the 

Univac 1103, one of the few large scientific computers 

available at the time. 

Funded primarily by a training grant from the 

National Institutes of Health for training biostatisticians, 

Minton established a new Department 

of Statistics at SMU in 1962. It was significant 

in obtaining faculty approval of the new department 

that he had provided research consultation in 

either statistics or computing—or both—to every 

department in the university. The new department 

offered master’s and doctoral degrees following the 

North Carolina model and received consultation 

and assistance from Cox. The department subsequently 

expanded to offer degrees at all levels, to 

provide consultative assistance to faculty research 

and outside clients, and to conduct research in statistical 

theory and methods. 

After 10 years of developing and administering 

the SMU department, Minton moved to 

Richmond, Virginia, to take the position of 

dean of the School of Arts and Sciences at 

Virginia Commonwealth University in 1972. 

There, in addition to his duties as dean, Minton 

formed an Institute of Statistics—a form of 

liaison office between the Department of 

Mathematical Sciences in the School of Arts and 

Sciences and the Department of Biostatistics in 

the Medical College of Virginia. He also was 

active in statistical consulting in local industry, 

including paper manufacturing, polymer plastics 

processors, pharmaceuticals, a national baking 

company, and a federal agency. He retired from 

VCU in 1988. That year, a special symposium 

was organized in his honor, attended by many 

former students and colleagues. 

Minton was the recipient of numerous academic 

and professional awards, including being named a 

Fellow of the American Statistical Association and 

being an early recipient of the ASA Founders Award 

for service to the association and the profession. In 

his honor, the SMU Statistics Department created 

the Paul Minton Award for the student who scores 

highest on the basic qualifying examination. Also, 

the ASA created the Paul Minton Service Award, 

which has been given annually since 1992. He was a 

longtime member of the ASA, IMS, and Biometric 

Society. He founded the North Texas Chapter of 

the ASA and was active in a variety of roles during 

the years he was in Dallas. Later, he was very 

active with the Virginia Academy of Sciences (the 

Virginia Chapter of the ASA) and in organizing the 

Southern Regional Conferences on Statistics. He 

participated in numerous committees, task forces, 

councils, and boards, and he served one term as 

vice president of the ASA. 

Minton had an active life outside the statistics 

profession that included a love of classical 

music and the ability to create terrible puns. He 

combined those interests with his knowledge of 

statistics in the following way. As part of the 

ASA’s 150 celebration in 1989 in Washington, 

DC, he and some friends created a “statistical 

songbook,” from which they performed at 

a banquet held on that occasion. In the hands 

of these statistical punsters, the tune “Bali 

Hai” from the musical “South Pacific” became 

the results for a test of statistical significance: 

“Barely High.” These and other rewritten song 

lyrics can be found in the songbook, which was 

subsequently published in the August 1990 

issue of The American Statistician. 

In a sense, entertaining audiences with humorous 

statistical lyrics to well-known songs became 

another way for Minton to be an advocate for 

statistics and promote the profession to a wider 

world. His life and work as a statistician were 

summarized at his memorial service in the words 

of his son, Roland: “He recruited students to 

the newly developing field of statistics, he found 

money to support them during their student 

days, and he helped them find jobs after they 

graduated. Others have said of him that he was 

a gentle person, with a gentle temperament, but 

he changed the lives of hundreds, if not thousands 

of statistical students, just as he changed 

the lives of many others who knew him outside 

the profession.” 

A memorial session for Minton was held at the 

2008 Joint Statistical Meetings in Denver. Papers 

from that session will be included in the ASA 

Archives Collection at Iowa State University. ■

Mollie Orshansky: 

Author of 

the Poverty 

Thresholds 

Gordon M. Fisher 

Mollie Orshansky was born on January 9, 

1915, in the Bronx in New York City. 

She was the daughter of Ukrainian- 

Jewish immigrants who spoke limited English. 

Although her father worked hard at a number of 

jobs, Orshansky and her sisters grew up poor—in 

her words, the family could “barely … make ends 

meet.” The girls slept two to a bed and wore handme-down 

clothing. Orshansky remembered going 

with her mother to stand in relief lines for surplus 

food. As she was to say later, “If I write about the 

poor, I don’t need a good imagination—I have a 

good memory.” 

Orshansky was both the first high-school 

graduate and first college graduate in her family. 

She graduated from Hunter College High School 

in Manhattan (then a school for gifted young 

women) in 1931. Because she received two scholarships 

from the college, she was able to attend 

Hunter College (at that time a women’s college). 

She graduated from Hunter in 1935 with an AB 

in mathematics and statistics. She was a statistician 

by training and profession, although she has sometimes 

been referred to as an economist. 


Editor’s Note: Views expressed in this article are those of the author, and should not be construed as representing the 

policy of the U.S. Department of Health and Human Services. This article is condensed from an unpublished draft 

paper with references. That paper is based on extensive research involving published articles and documents, unpublished 

documents, and conversations with Mollie Orshansky. 

A daughter from a poor family graduating from 

college during the Great Depression, Orshansky did 

not have the luxury of attending graduate school 

before she started working. Instead, she found a job 

as a statistical clerk in the New York Department of 

Health’s Bureau of Nursing, where she worked on 

infant mortality and other subjects for a year. 

In 1936, the U.S. Children’s Bureau (then part of 

the U.S. Department of Labor) offered Orshansky a 

job as a junior statistical clerk. She accepted the job, 

moving from New York to Washington, DC. Her 

first job assignment involved logarithmic equations 

for 600 infants who had been in a study. In July 

1939, the bureau promoted her to research clerk, a 

job in which she stayed until January 1942, working 

on biometric studies of child health, growth, 

and nutrition. 

It was while she was working at the U.S. 

Children’s Bureau that Orshansky began taking 

graduate courses. At various times between 1936– 

1937 and 1960, she took courses in economics and 

statistics at the U.S. Department of Agriculture 

Graduate School and American University. From 

January 1942 to March 1943, Orshansky took a job 

Courtesy of the Social Security Administration Archives 



How Mollie Orshansky 

Developed the Poverty Thresholds 

When Orshansky developed her poverty thresholds, she 

made use of information she had worked with at the U.S. 

Department of Agriculture (USDA). She based her thresholds 

on the economy food plan, which was the cheapest of four 

food plans (hypothetical food budgets providing nutritious 

diets at different cost levels) developed by USDA. 

From a finding of USDA’s 1955 Household Food Consumption 

Survey (the latest such survey available during the early 

1960s), she knew families of three or more persons had spent 

approximately one-third of their after-tax money income on 

food in 1955. The way in which she used this survey finding 

was by considering a hypothetical average family that was 

spending one-third of its income on food and by assuming 

the family had to cut back on its expenditures sharply. She 

assumed expenditures for food and non-food would be cut 

back at the same rate so the family would continue to spend 

a third of its income for food. 

When the food expenditures of the hypothetical family 

reached the cost of the economy food plan, she assumed 

the amount the family would spend on non-food items 

would also be minimal, but adequate. (Her procedure did 

not assume specific dollar amounts for any budget category 

besides food.) Following this logic, she calculated poverty 

thresholds for families of various sizes by taking the dollar 

costs of the economy food plan for families of those sizes and 

multiplying the costs by a factor of three—the “multiplier.” 

(She followed somewhat different procedures to develop 

thresholds for two-person and one-person units.) 

She differentiated her thresholds not only by family size, but 

by farm/nonfarm status, by the gender of the family head, by 

the number of family members who were children, and (for 

one- and two-person units only) by aged/non-aged status. 

The result was a detailed matrix of 124 poverty thresholds, later 

reduced to 48. 

To avoid confusion, the preceding explanation has been 

phrased in terms of the economy food plan. However, 

Orshansky actually developed and discussed two sets of 

poverty thresholds, one derived from the economy food plan 

and one derived from the somewhat less stringent low-cost 

food plan. (The latter set was the one she preferred.) It was the 

lower of the two sets of poverty thresholds—the set derived 

from the economy food plan—that the Office of Economic 

Opportunity adopted as a working definition of poverty in 

May 1965. One probable reason for the adoption of the lower 

set of thresholds was that the lower set yielded approximately 

the same number of persons in poverty as the Council of 

Economic Advisers’ $3,000/$1,500 poverty line. 


as a statistician for the New York City Department of Health, working 

on a survey of the incidence and therapy of pneumonia. 

Talking about her government career, Orshansky once commented, 

“I basically always worked with women, except when I was in the 

war agencies [the National War Labor Board during World War II 

and the Wage Stabilization Board during the Korean War].” During 

World War II, in particular, large numbers of men left civilian jobs to 

serve in the military. As a result, a number of female workers were able 

to get jobs that would not have been open to them under ‘normal’ 

circumstances. Describing the situation for female federal workers, 

Dorothy Rice, a colleague of Orshansky’s, later said, “Any female that 

had anything on the ball really did very well during the war. All the 

men went to the war and we had to carry on.” 

In March 1943, Orshansky secured a job at the U.S. National War 

Labor Board as chief of the Program Statistics Division of the Research 

and Statistics Branch. She planned and executed the collection and 

analysis of data required for the board’s decisions on wage adjustments 

and the effects of the board’s stabilization activities. She stayed in this 

job until September 1945, when the operations of the board were 

being terminated. 

From September 1945 to March 1951, Orshansky worked as 

a family economist at the Bureau of Human Nutrition and Home 

Economics of the U.S. Department of Agriculture (USDA). She conducted 

research in family consumption and levels of living, carrying 

out a variety of assignments. She was in charge of preparing data on 

the estimated value of household furnishings and equipment on farms 

for the department’s balance sheets of agriculture for 1947–1950. 

In 1948, Orshansky and a colleague were responsible for responding 

to letters from members of the public asking how they could make 

ends meet on their existing income in the face of the severe (by U.S. 

standards) post–World War II inflation. Orshansky and her colleague 

would send the letter writers pamphlets about preparing a family budget 

and planning low-cost and moderate-cost meals using USDA’s 

food plans—hypothetical food budgets providing nutritious diets at 

different cost levels. (This shows Orshansky was working with USDA’s 

food plans at least 15 years before she was to use them to develop her 

poverty thresholds.) 

About 1949, Orshansky carried out an assignment to update a standard 

budget (an estimate of necessary living costs) for a single working 

woman in New York. In June 1950, she presented a paper titled 

“Equivalent Levels of Living: Farm and City” at the annual meeting 

of the Conference on Research in Income and Wealth (CRIW). Her 

paper and the other papers presented at the meeting (including one 

by Milton Friedman) were published in volume 15 of CRIW’s annual 

(and still continuing) series, Studies in Income and Wealth. 

In March 1951, during the Korean War, Orshansky secured 

a job at the U.S. Wage Stabilization Board as the director of the 

Program Statistics Division of the Office of Economic Analysis. She 

planned and directed the board’s statistical program until August 

1953, when the operations of the board were being terminated due 

to the end of the war. 

In 1952, while Orshansky was working at the U.S. Wage 

Stabilization Board, she was elected a member of the Econometric 

Society (an international society for the advancement of economic 

theory in its relation to statistics and mathematics). Of the 73 candidates 

for election to membership in 1952, Orshansky was the 

only woman.

From August 1953 to February 1958, Orshansky worked as a food 

economist for USDA in a successor office to the Bureau of Human 

Nutrition and Home Economics, where she had worked before. She 

planned and directed the collection and analysis of data on food consumption 

and expenditures of American households. 

In 1956 and 1957, Orshansky was the senior coauthor of two 

reports on family food expenditures and food consumption based on 

a food consumption survey of rural families in the North Central 

[Midwest] region. She was one of a number of people who gave technical 

assistance in the preparation of a series of reports on USDA’s 

1955 Household Food Consumption Survey, and she wrote a 

major section of a summary report on the same survey. This 1955 

Household Food Consumption Survey was the source from which 

Orshansky would calculate the “multiplier” she later used to develop 

her poverty thresholds. 

In February 1958, Orshansky went to work for the Social Security 

Administration (SSA) in an office that later became the Office of 

Research and Statistics (ORS). ORS seems to have been one of a 

small number of federal offices that provided significant work opportunities 

for women professionals at this time; during Orshansky’s first 

decade there, both the director and the deputy director were women. 

Orshansky had several titles at SSA, but can best be described as a 

social science research analyst. 

Orshansky performed a number of assignments during her early 

years at SSA. Her first was to prepare an article on standard budgets 

(family budgets) and practices in setting fee scales in 21 large cities. 

She also prepared several annual updates of an analysis of the income 

sources of “young survivors” (widows under age 65, particularly those 

with minor children). She prepared a medical care standard for the 

Budget for an Elderly Couple, of which the Bureau of Labor Statistics 

(BLS) was preparing an interim revision. She also prepared data for 

16 charts on financial resources of the aged for the Chart Book for the 

1961 White House Conference on Aging. 

While Orshansky’s development of the poverty thresholds was a 

major milestone in both social policy history and statistical history, it 

grew out of ordinary work activities—an “answer for the record” for 

a congressional hearing and an in-house research project. During a 

1960 congressional hearing, a senator asked HEW Secretary Arthur 

Flemming if he had figures on how much it costs a retired couple to 

live. Flemming said HEW would provide an answer for the record, 

and Orshansky was the civil servant who prepared an unattributed 

submission for the record. She mentioned the Budget for an Elderly 

Couple, which BLS was then revising, and a similar budget prepared 

by a group in New York. In addition, she provided two rough measures 

of income inadequacy for an elderly couple that she developed 

by applying multipliers derived from USDA’s 1955 Household Food 

Consumption Survey to the cost of USDA’s low-cost food plan (at that 

time, the cheapest of USDA’s three food plans)—almost exactly as she 

was to do several years later in her 1963 and 1965 poverty articles. 

In early 1963, Orshansky was assigned to do an in-house research 

project on poverty as it affects children. At that time (the year before 

the War on Poverty was declared), there was no generally accepted 

measure of poverty, so to carry out this research project, Orshansky 

developed one (see “How Mollie Orshansky Developed the Poverty 

Thresholds” on the previous page for her methodology). In July 1963, 

she published results of her research project in a Social Security Bulletin 

article, “Children of the Poor,” in which she also described the initial 

version of her poverty thresholds. 

Orshansky told several interesting stories about 

events during the development of her thresholds: 

➤ As noted in the sidebar, “How Mollie Orshansky Developed the 

Poverty Thresholds,” Orshansky based her thresholds on USDA’s 

economy food plan. She had been working with the USDA food 

plans at least as early as 1948, and so was familiar with them. In 

the context of developing poverty thresholds for families, she 

became concerned about the economy food plan not allowing 

for purchases of food away from home, either at work or school. 

For the purpose of developing poverty thresholds, she wanted 

to modify the cost of the food plan by adding $0.15 a day per 

person to it to allow for the husband in a family to buy coffee 

at work or for children to buy snacks. However, her supervisor 

would not allow her to do so. 

➤ One major source for Orshansky’s July 1963 article was a 

special tabulation of Current Population Survey (CPS) data that 

the Social Security Administration (SSA) purchased from the 

U.S. Census Bureau for $2,500. The results showed the median 

annual income of nonfarm, female-headed families with 

children was $2,340. Orshansky was horrified when she realized 

half of these families lived for a year on less than SSA paid for 

one statistical tabulation. She later commented, “I determined I 

was going to get my $2,500 worth.” 

➤In the CPS, thousands of sample cases represent millions 

of families and persons in the general population. Published 

U.S. Census Bureau reports based on survey data always 

show figures that relate to the national totals of families and 

unrelated individuals. However, when Orshansky got the results 

of her $2,500 tabulation of CPS data, they gave her only the 

unweighted counts of sample households in various poverty 

and nonpoverty categories; SSA’s payment to the bureau had 

not been enough to pay for computing weighted national 

totals for her, so she had to do the work herself, “by hand,” to 

calculate weighted national totals from the unweighted sample 

count. She also calculated the poverty gap “by hand” for her 

January 1965 article. She didn’t even use a calculator. 

In January 1964—only six months after the publication of 

Orshansky’s obscure article—President Lyndon Johnson declared a 

war on poverty. In a chapter on the problem of poverty in its 1964 

annual report, the president’s Council of Economic Advisers (CEA) 

put forward its own rough measure of poverty: $3,000 for families 

of all sizes and $1,500 for unrelated individuals. The CEA’s $3,000 

figure was not derived in any way from Orshansky’s work; however, 

the CEA report did cite two dollar figures from Orshansky’s July 1963 

article (without giving her name as the author) to show that the CEA’s 

$3,000 figure was a reasonable level for a poverty line. 

When Orshansky saw the January 1964 CEA report (including the 

reference to her dollar figures), she was disturbed by the CEA’s failure 

to vary its $3,000 family poverty line by family size, as this resulted 

in understating the number of children in poverty relative to aged 

persons. The CEA figure “led to the odd result that an elderly couple 



Mollie Orshansky (third from left) at the first Conference on Women in the War on Poverty on May 8, 1967. With her are Mary Keyserling, Josephine 

Weiner, and Hyman Bookbinder. Near the end of her speech, Orshansky said, “…our statistics, imperfect though they may be, show us where problems 

are even if they cannot always reveal exact dimensions…[C]alculations … relating to poverty … exist only to help make them disappear, and so 

if we can think bold solutions and dream the big dream, we can wipe out the scourge of poverty before we all agree on how to measure it.” 

with $2,900 income … would be considered poor, but a family with 

a husband, wife, and four little children with $3,100 income would 

not be.” In addition, the president’s declaration of a war on poverty 

evidently led SSA to give a higher priority to Orshansky’s poverty 

work. As a result, Orshansky’s supervisors asked her to do an analysis 

extending her families-with-children poverty thresholds to the whole 

population. She completed this analysis in late 1964 and it was published 

in the Social Security Bulletin in January 1965 as “Counting the 

Poor: Another Look at the Poverty Profile.” 

The publication of Orshansky’s January 1965 article came when 

the Office of Economic Opportunity (OEO)—the lead agency for 

the War on Poverty—was being set up. OEO officials were enthusiastic 

about Orshansky’s poverty thresholds, considering them to be 

an advance over the CEA’s $3,000-for-all-family-sizes figure. OEO 

research chief Joseph Kershaw commented, “Mollie Orshansky says 

that when you have more people in the family, you need more money. 

Isn’t that sensible?” In May 1965, OEO adopted Orshansky’s thresholds 

as a working definition of poverty for statistical, planning, and 

budget purposes, and, in August 1969, her thresholds were made the 

federal government’s official statistical definition of poverty. 

When she developed the poverty thresholds, Orshansky was “an 

obscure civil servant” who worked “[d]own a dimly lit hall, among 

stacks of computer printouts [at] a paper-covered desk …” However, 

after her thresholds were adopted as the federal government’s poverty 

line, she became much more well known. Because of frequent citations 

of her work in academic articles and books, someone once referred to 

her as “the ubiquitous footnote.” Besides presenting papers at a number 

of professional meetings and publishing a number of articles, she 

testified and/or provided written documents to congressional committees 

on 10 occasions between December 1967 and 1990. 

On five occasions between 1968 and 1980, Orshansky participated 

in federal interagency committees that reviewed the poverty thresholds. 

The 1968–1969 committee made two modest revisions in 

the thresholds, and it was the thresholds with these revisions that 

were made the official federal statistical definition of poverty. In 

1981, several minor changes recommended by the 1979–1980 

committee were made. 

Following up on a 1973 subcommittee’s recommendation for a 

new income survey vehicle, the HEW Technical Working Group on 

income data and models proposed that a new survey be developed 

to provide better information on the income and related characteristics 

of the population and on participation in government programs. 


Orshansky was a member of this group. The technical working group 

reviewed and contributed to the plan for what became the Income 

Survey Development Program—the research and development phase 

for the Survey of Income and Program Participation. 

In 1982, Orshansky retired from SSA after a government career 

that lasted for more than 40 years. She died on December 18, 2006, 

in New York City. 

Orshansky received a number of honors for her achievements. 

She received a Commissioner’s Citation from the Social Security 

Administration in 1965 for her creative research and analytical work 

and the Distinguished Service Award (the department’s highest recognition 

of civilian employees) from HEW in 1976 for her “leadership 

in creating the first nationally accepted measures of income adequacy 

and applying them diligently and skillfully to public policy.” In 

1974, she was elected a Fellow of the ASA for her leadership in the 

development of statistics for the measurement of poverty. After her 

retirement, she received a national award from the Children’s Defense 

Fund in 1989 and an Award for Distinguished Contribution from the 

American Political Science Association in 1993. 

Orshansky’s achievements also were recognized in a very different 

setting. She may be the only statistician to have been discussed on a 

major television show. One subplot of “The Indians in the Lobby,” 

an episode of “The West Wing” originally broadcast in November 

2001, involved the adoption of a new poverty measure, and one character 

alluded to how Orshansky developed the current poverty measure. 

While the discussion of issues relating to a new poverty measure 

sounded plausible, the episode grossly mischaracterized the rationale 

for Orshansky’s methodology for developing the poverty thresholds. 

Of the contributions to American public policy that Orshansky 

made during her career, the greatest by far was her development of the 

poverty thresholds. The poverty line has become a major feature of 

the architecture of American social policy. Although the measure may 

have its shortcomings, the poverty line gives us a means of identifying 

and analyzing the makeup of the groups in our society with the least 

resources. Orshansky’s thoughtful analyses of the poverty population 

began a tradition, and there are now numerous researchers and advocates 

who conduct such analyses and draw policy implications from 

them. Even though there may not be consensus on answers, the question 

“How does it affect the poor?” has become a test for proposed 

policies and programs. And a simplified version of the poverty line is 

used to determine eligibility not only for certain federal programs, but 

for a number of state, local, and private programs, as well. ■

Th e ASA 

Brings the Library 

to Your Desktop 

Did you know your ASA membership includes online access to the 

Journal of the American Statistical Association 

Journal of Business & Economic Statistics 

and 

Th e American Statistician? 

Log in at MEMBERS ONLY today to access your journals! 

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Day in the 

LIFE 

9 

1

Life as a Stochastic Modeler 

Alan Gelfand, Duke University 

First, let me say I am flattered to be invited to write about my 

career in statistics. I do not know if many readers will find it 

interesting, but I do know it has evolved along a path that 

may be viewed as a reflection of the evolution of our field. 

What I have become is a stochastic modeler. In particular, 

a modeler whose primary focus is in the area of environmental 

processes, including ecological systems, exposure assessment, and 

climate processes. A common ingredient of this analysis is the collection 

of data across space and, often, across time. I am usually 

studying a complex process with different types of information— 

theoretical results drawn from physical principles, mechanistic 

insights based on knowledge of aspects of process function, and 

empirical knowledge as a result of previous data collection and 

relevant laboratory and field experiments. 

I imagine the process is described at multiple levels, with the 

foregoing information entering in at different places and levels. 

I typically represent the process through an acyclic-directed 

graph with some nodes observed and others unknown, and then 

I infer about the unknown nodes given the observed nodes. I 

formulate the joint model in a hierarchical fashion driven by 

the graph and fill in the stochastic details needed to complete 

the model specification. In essence, a stochastic modeler seeks 

the posterior distribution of what we don’t know given what we 

have observed, so we usually fit these graphical models within 

the Bayesian framework. 

It has emerged, more clearly than ever, that such modeling is 

my greatest strength as a statistician. Moreover, being part of a 

team of researchers assembled to ‘brainstorm’ a complex problem 

is an exceptionally stimulating and rewarding activity. In 

this setting, the modeler becomes a central player in synthesizing 

inputs from team members, shaping progress on the problem, 

and becoming a richer scientist as a result. 

I think this research view serves as a contemporary perspective 

of our field. The team research concept, which it presumes, dramatically 

revises the role of the statistician from someone brought 

in at the end to carry out data analysis and create ‘pretty’ pictures. 

Rather, the statistician is able to illuminate what we can learn with 

what we have, as well as what we need to collect to learn about 

what we want. In the midst of all this, it is essential that the modeler 

retain technical rigor, attention to detail, and appreciation of 

the properties and features encompassed by the modeling. 


How Did I Get Here? 

So, that is the story of what I do. But, how did I get here? I followed 

a fairly typical course for my time, starting as an undergraduate 

major in mathematics. In my senior year, I took an 

introduction to mathematical statistics course from the book by 

the same name written by Robert Hogg and Allen Craig (embarrassingly, 

using the first edition, spanning all of 240 pages). The 

material was so elegant, I was smitten, and I applied to graduate 

programs in statistics, moving to Stanford—as far from New 

York City as I could go. 

I thoroughly enjoyed my four years at Stanford. (The late 

1960s was a remarkable time for a statistics department in the 

San Francisco Bay area—and for the country.) I emerged with 

training as a mathematical statistician, really the only path available 

at that time. As I have told many people, I had a ‘wasted 

youth’ in some ways, not discovering until the late 1980s that I 

was born to be a Bayesian (and, with it, to be a modeler). 

People who influenced me include my adviser from Stanford, 

Herbert Solomon, a wonderful, generous man who was particularly 

good at recognizing how people could best contribute to 

projects. He was one of the earliest ‘team builders’ in our field. 

Charles Stein, who was also from Stanford, influenced me in 

a distant way. I believe I hold the record for the most courses 

ever taken from Stein by a single student. His beautiful, deep 

thoughts in decision theory led me down that research path, 


which led me to empirical Bayes and, eventually, research within 

the Bayesian paradigm. Finally, Adrian Smith was most influential 

to me. Following conversations with him in the mid- to late 

’80s, I arranged a sabbatical to work with him at Nottingham 

University. I arrived seeking to use his numerical integration 

software for certain empirical Bayes problems and left with the 

Gibbs sampler! 

In this regard, I was fortunate to be involved in a seminal contribution 

to our field. Indeed, working with Smith, it was a remarkable 

feeling to come upon the Gibbs sampler from the 1984 paper 

of Stuart Geman and Donald Geman, “Stochastic Relaxation, 

Gibbs Distributions, and the Bayesian Restoration of Images,” and 

recognize it was better suited for handling Bayesian computation 

than it was for its original purpose—sampling Markov random 

fields. In fact, for the first half of the 1990s, I spent almost all 

my time writing as much about the use of Gibbs sampling and 

Markov chain Monte Carlo algorithms for Bayesian model fitting 

and model determination as I could. These were heady times for 

those of us involved, as we realized we were elaborating on a technology 

to successfully address the problem of Bayesian computation—to 

replace high-dimensional integration with simulation 

from high-dimensional distributions. 

In the mid 1990s, I started moving from research confined to 

computational matters to the analysis of spatial and spatial-temporal 

data. Again, I was fortunate. Spatial statistics had struggled as 

LATEX 

LATEX 

LATEX 

MacKichan 

SOFTWARE, INC.

a field, functioning at the periphery of mainstream statistical 

work, and was particularly in need of inexpensive, high-speed 

computation to enable tools vital for visualization. Fortunately, 

this computing power arrived and, with it, the advent of geographic 

information systems (GIS) software to create effective 

maps that tell a broad range of stories. However, GIS software 

had an obvious limitation from a statistician’s perspective—it was 

descriptive, but not formally inferential. A wonderful opportunity 

for stochastic modeling revealed itself, an opportunity with 

few players at the time and a growing need in the applied community 

as more data with spatial content were being collected. 

Here, again, there was more good fortune for me, because 

advantages to hierarchical modeling for analyzing spatial data 

became evident—the advantages we attach to working in general 

in the Bayesian framework: fully model-based inference with 

accurate assessment of uncertainty. Additionally, the customary 

asymptotics, which are employed in time series, so-called expanding 

domain asymptotics, may be inappropriate for spatial data 

where infill asymptotics are perhaps more relevant. However, 

that the latter asymptotics typically reveal information about 

unknowns is bounded in terms of inference. In this regard, the 

Bayesian framework provides exact inference, avoiding possibly 

inappropriate asymptotics. Of course, the implied caveat is that 

the data never overwhelm the prior; we must be more attentive 

to prior sensitivity than in other areas. 

Today, my good fortune persists through continuing research 

relationships with talented former students. In particular, 

Sudipto Banerjee and Brad Carlin, both at the University of 

Minnesota, invaluably helped to shape my spatial data research 

agenda. During my career, I also found wonderful interdisciplinary 

collaborators. When I was at the University of Connecticut, 

I worked seriously at building bridges with ecology and evolutionary 

biology, yielding productive research relationships with 

John Silander and Kent Holsinger. Moving to Duke University 

made things even better, as Duke is a naturally collaborative 

institution; research teams develop over the campus with strong 

encouragement from the university. What program on campus 

is more naturally interdisciplinary than statistics? Now at Duke, 

I have productive relationships with researchers in the Nicholas 

School of the Environment, particularly Jim Clark and Marie 

Lynn Miranda. 

In summary, it seems my career has been one of ongoing good 

luck. To some extent, I believe this is true, but I also believe 

you can make your own luck. Scientific curiosity, receptivity to 

reading in other fields, availability for collaboration, willingness 

to listen, and attention to effective communication all facilitate 

creating opportunities. In addition, developing the full toolkit— 

strong theoretical training, stochastic modeling expertise, modern 

computing skills, and data analysis experience—enables one 

to take advantage of these opportunities. Some of us work better 

with certain parts of the toolkit and enjoy certain parts more 

than others; however, as statisticians in the 21st century, we can 

each find our own way to contribute and, in that sense, we are 

all fortunate. ■ 

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On Becoming a Teacher 

Daren Starnes, The Lawrenceville School 

As a fledgling mathematician, I was blessed to learn from several 

outstanding teachers. First and foremost, there was 

Sammi Yopp , my second-grade teacher at the North Carolina 

Charlotte Country Day School. Yopp sparked my mathematical 

curiosity and cajoled me into playing chess as a means of honing my 

analytic thinking skills. Several years later, my mathematical abilities 

were challenged by Sue Sams, my ninth-grade honors algebra II/ 

trigonometry teacher at East Mecklenburg High School. Sams was, 

to put it mildly, a disciple of Mary P. Dolciani. The following year, 

I landed in the classroom of another Dolciani disciple, Roger 

Bruwhel at West Charlotte High School. Bruwhel was the one who 

encouraged me to leave my comfort zone by applying to the North 

Carolina School of Science and Mathematics (NCSSM). Off to 

NCSSM I went, where I was surrounded by a host of talented 

“mathletes,” budding scientists, and computer programmers. As a 

junior sitting in Dan Teague’s “Calculus with Topics” class one day, 

I made the decision to become a high-school math teacher. Why? 

Because I witnessed firsthand the tremendous passion, thirst for 

understanding, and knack for asking just the right question at the 

right moment that allows great teachers such as Teague to inspire 

students to do extraordinary things. 

From NCSSM, I went on to pursue a bachelor’s of science in 

mathematics with secondary teaching certification at the University 

of North Carolina, Charlotte (UNCC). At the time, UNCC was 

a teaching university, which enabled me to take all my classes with 

professors from the outset. At the urging of my professors, I participated 

in Budapest Semesters in Mathematics, where I fell in 

love with combinatorics and probability. Having never left the 

southern United States until that moment, I admit to experiencing 

a healthy dose of culture shock as a westerner in what was then 

socialist Budapest. 

From Eastern Europe, I accepted an offer to join a graduate 

program in pure mathematics at the University of Michigan. As I 

was nearing the end of my master’s degree requirements, it became 

1 


SEPTEMBER 2008 2008 AMSTAT AMSTAT NEWS NEWS 25 25

“ If I have become a teacher leader, it is 

because I have followed in the footsteps 

of the many great mentors and role models 

who have shaped my thinking. 

” 

clear to me that my interests had shifted dramatically away from 

classical pure mathematics in the direction of research methods, 

combinatorics and graph theory, and statistics. Ironically, my only 

statistical coursework to that point was a calculus-based probability 

and statistics course as a sophomore at UNCC. Having reached an 

important fork in the road, I opted to end my relationship with 

pure math and begin my high-school teaching career. 

Charlotte Country Day School (CCDS) was my proving 

ground, the place where I learned the craft of teaching from supportive 

colleagues such as Tim Timson, Sheila McGrail, and Sue 

Schwartz. My interest in curriculum development blossomed. 

CCDS launched the International Baccalaureate (IB) program, and 

I volunteered to take on the IB Higher Level Math course. In my 

fifth year there, McGrail and I designed a course called “Probability, 

Statistics, and Finite Math” to better serve the needs of students 

Environmental Section 

Upcoming ENVR Workshop! 

What: Statistical Issues in Monitoring the Environment 

When: October 22–24, 2008 

Where: National Center for Atmospheric Research 

(NCAR), Boulder, Colorado 

This workshop covers state-of-the-art applications and 

statistical methods in environmental monitoring. Sessions 

on applications include monitoring in ecology, monitoring 

in air quality, monitoring of aquatic resources, and 

monitoring of climate change and its impacts. The spatio-temporal 

data collected in environmental monitoring 

present interesting and challenging statistical problems, 

such as modeling of space-time correlation, analysis 

of the huge amount of correlated data, and analysis of 

high-frequency monitoring data. Technical sessions will 

cover recent developments in statistical methods for environmental 

data. A one-day short course on the analysis of 

spatial and spatio-temporal data will be offered and taught 

by Doug Nychka. There also will be a poster session. Poster 

abstracts need to be submitted by September 23, 2008. 

For more information, contact Hao Zhang, Department of 

Statistics, Purdue University, West Lafayette, IN 47906; (765) 

496-9548; zhanghao@purdue.edu. 


who were not bound for AP Calculus. When AP Statistics kicked 

off in 1996–1997, I happily agreed to play a leadership role. Better 

still, CCDS science colleague Wright Robinson convinced me to 

team teach AP Statistics with AP Environmental Science, to collaborate 

on a field and lab manual, and to co-present at several 

conferences. Robinson got me started as a writer, having himself 

authored several children’s books about ecological topics. More 

than anything, Robinson convinced me to “get out there” as a contributing 

member of my profession. 

When our youngest son graduated from CCDS, my wife and 

I headed west to The Webb Schools in California to begin the 

next chapter in our lives. I assumed leadership of the mathematics 

department, and with the support of some exceptional colleagues, 

began to tweak the existing curriculum. We dissolved our existing 

geometry course and created an integrated geometry with algebra 

course that continues to evolve today. In addition, we added a capstone 

course for our most talented math students titled “Calculus- 

Based Probability and Statistics.” 

Externally, my role with the College Board expanded dramatically. 

I led four or five one-day workshops for AP Statistics teachers 

every year and a similar number of week-long summer institutes. 

These opportunities to help coach other teachers gave me a chance 

to share some of the lessons I learned in the trenches. At the same 

time, interacting with other highly motivated professionals encouraged 

me to further refine my own teaching. 

In the summer of 1998, I began reading AP Statistics exams. 

Every year since, I have attended this annual gathering of highschool 

and college/university statistics teachers, which some of us 

refer to fondly as “stats summer camp.” For the past eight years, 

I have been privileged to serve as part of the reading leadership 

team, first as a table leader and, more recently, as a question leader. 

Needless to say, reading hundreds of exam papers every day is not 

the highlight of the experience for most of us. Instead, it’s the tremendous 

opportunity to network with like-minded professionals 

who have amassed an abundance of content knowledge and a host 

of resources for teaching introductory statistics. There are abundant 

opportunities for discourse during breaks, at lunch, in the evening 

social lounge, and at various nightly professional events. Truth be 

told, some of my best conversations about statistics teaching have 

taken place at AAA baseball games. 

Shortly after my wife and I moved west, Dan Yates contacted 

me about assisting him with some of the calculator features in the 

second edition of The Practice of Statistics, the successful highschool 

AP Statistics textbook he coauthored with David Moore. 

Having spent three years co-editing the Technology Tips column in 

the National Council of Teachers of Mathematics’ (NCTM) magazine, 

Mathematics Teacher, I felt comfortable accepting this role as 

a minor contributor in the writing process. Somewhere along the 

way, I mentioned to Yates that I had a few suggested enhancements 

to the text based on my experiences as a user. To my surprise, 

he called my bluff and asked me to compose a revised version of 

Chapter 1 that incorporated my recommendations. Doing so took 

far longer than I had expected, with many false starts along the 

way. The net result of my efforts was a 120-page tome disguised 

as a single textbook chapter. With a typical dose of his southern 

charm, Yates quipped, “You know, if chapters were this long, we’d 

have to package the book with a wheelbarrow.” In the end, I was

instructed to “trim” the chapter to 60 pages. That was probably the 

most painful editing task I have ever undertaken! (There was one 

consolation: Nearly all the trimmed material ended up in a supplement 

somewhere.) 

So, my second career as a writer was launched. I teamed with Yates 

and Moore on a second venture—Statistics Through Applications, a 

high-school textbook for non–AP Statistics courses—that was completed 

in 2004. With both of my coauthors now retired, I’ll have my 

hands full with future revisions. 

In 2004, I was appointed to the ASA/NCTM Joint Committee 

on Curriculum in Statistics and Probability by then NCTM president 

Johnny Lott. Over the years, the joint committee has been responsible 

for several significant projects to develop materials that promote 

K–12 statistics education, including the Quantitative Literacy Series, 

the Elementary Quantitative Literacy Series, the Science Education 

and Quantitative Literacy Series, and Data-Driven Mathematics. 

The joint committee also has assumed responsibility for designing 

teacher workshops to support effective classroom use of these materials. 

With the ASA’s recent publication of Guidelines for Assessment and 

Instruction in Statistics Education (GAISE), the committee has begun 

developing materials and training to help teachers implement the curricular 

framework described therein. Roxy Peck and I just completed 

a capstone module for high-school students, titled “Making Sense of 

Statistical Studies,” which began as an ASA Member Initiative several 

years ago when Peck, Henry Kranendonk, and June Morita were 

members of the joint committee. 


After a brief stint in independent school administration, I accepted 

an endowed chair position as Master Teacher in Mathematics at 

The Lawrenceville School this past fall. This unique position allows 

me to team teach with a different member of the department each 

trimester. By planning, instructing, and assessing collaboratively, 

we are able to improve our individual teaching practices. Our students 

also benefit from the distinct approaches and perspectives of 

their co-teachers. Other responsibilities as master teacher involve 

curriculum design, technology training, and professional development. 

Much of my time outside the classroom is spent in professional 

growth activities involving other teachers, either here at 

Lawrenceville or across the country at meetings, conferences, and 

workshops. Distilling that collective wisdom into better learning 

for students is at the heart of what I do on a daily basis. 

If I have become a teacher leader, it is because I have followed in 

the footsteps of the many great mentors and role models who have 

shaped my thinking. As my career has evolved, I have drifted from 

my origins as a pure mathematician into the realms of data analysis 

and statistics. All the while, my primary focus has remained squarely 

on how to help students learn mathematics (including statistics) 

in ways that will ensure their ability to use what they have learned 

to solve important problems today, tomorrow, and in the distant 

future. After all, if students see no potential to do anything productive 

with what they’re learning, why should they bother? ■ 


WASHINGTON 

OREGON 

Kristi Epke 

San Domenico School 

San Anselmo, California 

CALIFORNIA 

NEVADA 

“I am a high-school 

mathematics teacher, not a 

statistician, but I teach 

AP Statistics.” 

Graphic by Val Snider 

Harold Nelson 

Offi ce of Financial Management 

Olympia, Washington 

“My group does a lot of purely 

descriptive work, a lot of it 

centering on the lack of health 

care insurance.” 

IDAHO 

UTAH 

ARIZONA 

Willis Jensen 

W.L. Gore & Associates 

Flagstaff , Arizona 

“I’m the primary statistician for a 

portion of a medical device 

manufacturing company.” 

Where Are Your 

Colleagues … 

MONTANA 

WYOMING 

NEW MEXICO 

Geoff rey Bohling 

Kansas Geological Survey 

Lawrence, Kansas 

COLORADO 

Nancy Wang 

MDS Pharma Services 

Lincoln, Nebraska 

“My work is aimed at fusing 

information to reduce the 

non-uniqueness in subsurface 

characterization problems.” 

NORTH DAKOTA 

SOUTH DAKOTA 

“[I] use statistics in the 

design and analysis of phase I 

and IIa clinical trials.” 

Stephanie Lopez Cano 

The University of Texas 

San Antonio, Texas 

“Aside from teaching statistics in 

the classroom, I am director of the 

Statistical Consulting Center.” 

NEBRASKA 

KANSAS 

TEXAS 

M 

OKLAHOMA

MA 

MINNESOTA 

IOWA 

MISSOURI 

ARKANSAS 

WISCONSIN 

LOUISIANA 

Robert 

Kabacoff 

Management Research Group 

Portland, Maine 

“I use statistics to develop 

predictive models of leadership 

success in various industries and 

job functions.” 

ILLINOIS 

MISSISSIPPI 

MICHIGAN 

INDIANA 

TENNESSEE 

KENTUCKY 

ALABAMA 

OHIO 

And What Are 

They Doing? 

Liam O’Brien 

Colby College 

Waterville, Maine 

“I teach statistics to 

undergraduates and research 

statistical applications in 

public health.” 

James Hardin 

University of South Carolina 

Columbia, South Carolina 

“I use statistics to analyze 

data collected in various 

health studies.” 

Cindy Ammons 

WEST 

VIRGINIA 

Chapel Hill-Carrboro Schools 

Chapel Hill, North Carolina 

“I teach AP Statistics to 

high-school students.” 

GEORGIA 

Luke Davulis 

Maine Department of Labor 

Augusta, Maine 

“I work at gathering the data 

that form estimates of 

employment and wages by 

occupation.” 

NEW YORK 

PENNSYLVANIA 

SOUTH 

CAROLINA 

FLORIDA 

VIRGINIA 

NORTH 

CAROLINA 

VERMONT 

Malay Ghosh 

University of Florida 

Gainesville, Florida 

NEW JERSEY 

MASSACHUSETTS 

DELAWARE 

MARYLAND 

“I teach statistical theory and do 

research in both statistical theory 

and its applications.” 

MAINE 

NEW 

HAMPSHIRE 

RHODE 

ISLAND 

CONNECTICUT 

WASHINGTON, DC


30 

30 AMSTAT 

AMSTAT 

NEWS 

NEWS 

SEPTEMBER 

SEPTEMBER 

2008 

2008 

What Does a Teaching 

Associate Professor Do? 

Pam Arroway, North Carolina State University 

For about a decade now, the Department of Statistics at North 

Carolina State University has been hiring faculty in career 

path, non-tenure-track teaching positions. These positions 

were first called clinical assistant professor positions, but have since 

been renamed teaching assistant professors. These positions are 

unusual in that they are not tenure-track, but they do have a career 

track, unlike instructor, lecturer, or adjunct positions found at 

many universities in the United States. Thus, a teaching assistant 

professor has the opportunity to be promoted to a teaching associate 

professor and a teaching professor. For simplicity, I’ll refer to a 

person in any of these positions as a TAP and to tenure-track or 

tenured faculty as TT faculty. 

What Do TAPs Do? 

Currently, the department has four TAPs, all of whom began at 

the assistant level and were promoted to the associate level. All 

currently have five-year contracts and PhDs in statistics or mathematics. 

Their responsibilities include a nominal teaching load of 

four courses per semester, though most TAPs begin with a “3+” 

contract. That is, the teaching load is three courses per semester, 

plus some scholarly activity. That may include research, but 

doesn’t have to. 

The four TAPs (including me) have evolved into very different 

roles within the department. The ways we contribute to the 

department, university, and profession include teaching graduate 

and undergraduate courses for statistics majors and as service 

courses; developing curricular and teaching materials to share 

with colleagues and/or publish; writing research papers; serving in 

administrative roles such as assistant department head, co-director 

of graduate programs, and director of undergraduate programs; 

recruiting, advising, and mentoring undergraduate and graduate 

students, including directing dissertation research; writing grant 

proposals; providing professional service by reviewing journal submissions; 

being an associate editor or grading AP Statistics exams; 

serving on committees… Sounds a lot like what a TT faculty member 

might do, right? 

So what does a TAP not do? In my department, there’s very little 

we can’t do. We have the opportunity to achieve our full potential. 

The biggest difference between a TAP and TT position is we allocate 

our time differently to the realms of teaching, research, and 

service. TAPs are not expected to publish in top statistical journals, 

advise a regular stream of graduate students, or get research grants, 

though we can do those things. 

We are expected to do a lot of outstanding teaching, and the 

flexibility of these positions has allowed us opportunities junior

Teacher Associate Professor (TAP) and Director of Undergraduate Programs Roger Woodard 

(center) mentors North Carolina State graduate students. 

faculty at a Research I university can rarely afford to consider. For 

example, we spend time on large course redesigns; organize conferences; 

develop teaching materials that are worth sharing and 

publishing; take on administrative roles; and go after grants for 

REUs, conferences, undergraduate and graduate support, enhancing 

diversity, and teaching activities. 

I want to note that a TAP is not the same as a TT person doing 

research in statistics education. We have recently hired someone in 

this type of TT position to begin in January 2009. She will have 

the same research, teaching, and service expectations as any other 

TT faculty, with her research focused on statistics education. A few 

other statistics departments have, or at least have advertised, similar 

positions. While some TAPs are active in the national statistics education 

community, there is not a requirement of education research 

for reappointment or promotion. 

Why Would Someone Want 

to Be a TAP? 

A TAP is typically someone who is passionate about teaching and 

mentoring students and who wants to be in a research-intensive 

environment. A TAP position may look similar to a TT position at 

an institution that places more emphasis on teaching and undergraduates. 

However, there are advantages to being in a Research I 

school and NC State, in particular. 

There are resources that may not be available at smaller institutions, 

such as for travel and professional development. In addition 

to supporting NC State’s well-established undergraduate program, 

we have opportunities to mentor graduate students in teaching, 

research, or in their graduate careers in one of the oldest, largest, 

and best graduate programs in the United States. In a large department, 

we have a variety of undergraduate and graduate classes to 

teach and are surrounded by colleagues with various expertise. 

Opportunities also exist to provide statistical consulting for students 

and postdoctoral fellows in other departments. We can have 

Teacher Associate Professor (TAP) Pam Arroway serves 

as assistant department head and codirector of graduate 

programs at North Carolina State University. 

a big effect on the profession and in training critical thinkers. A 

variety of seminars (Bayesian, bioinformatics, biostatistics, environmental 

statistics, statistical genetics, regular departmental seminars, 

and seminars by a number of interview candidates) and special 

topics courses in the department and at the nearby Statistical and 

Applied Mathematical Sciences Institute provide opportunities for 

continuous professional development. 

Why Does the Department 

Want TAPs? 

The existence of TAPs at NC State does not mean TT faculty 

members are not excellent teachers. In fact, many have received 

university and national teaching awards. TAPs can make valuable 

contributions to the long-term goals of the department and the 


Teaching Associate Professor Kim Weems at the presentation of an 

honorary doctorate from North Carolina State to David Blackwell 


Teaching Associate Professor 

Roger Woodard joined 

the department in 2003 and 

currently serves as director of 

undergraduate programs. 


Kim Weems joined the 

department in 2001. 


Jeff Thompson joined the 

department in 2003. 

Teaching Associate Professor Kim Weems (left), David Blackwell, and 

Department Head Sastry Pantula 

mission of the university. The creation of TAPs incorporates faculty 

members with different interests and skill sets and helps keep 

teaching loads for research-active faculty at a level where they can 

continue to be successful in their research productivity. 

Unlike short-term adjuncts or instructors, TAPs have a vested 

interest in the future of the department. So, while it is more of a 

commitment than hiring an instructor, the benefit to the department 

tends to be much greater. TAPs have the time for (and even 

expectation of) improving teaching and curriculum across the 

whole department. NC State TAPs have also taken on administrative 

and service roles that lessen the non-research workload of TT 

faculty, make use of the professional strengths of those in TAP 

positions, and contribute to the long-term growth of the department. 

TAPs also play an important role in training graduate students, 

especially for academic positions. 

And About That Pesky Tenure … 

I don’t miss it. Is my position less secure than my colleagues? My 

contract has to be renewed every five years. With reappointment, 

promotion, and post-tenure review every three to five years, I’m 

not sure TT faculty members have it any easier. The university, 

which regards us as “special ranks” faculty, has explicitly stated that 

the review process is the same for TAPs as for TT faculty, though 

contributions in different realms must obviously be weighted differently. 

Industry isn’t handing out five-year contracts either. Job 

security is not a bigger concern for me than for anyone else. 

Some professional organizations have been outspoken against 

increase in non-TT positions in academia. I agree that non-TT 

positions should not increase at the expense of TT positions. They 

should be used to complement TT faculty members and support 

the mission of the university. Universities must be ethical and 

respectful in how non-TT faculty members are treated. A TAP is 

not a person the department gets to abuse and then dump when 

funding gets tight. Neither are TAPs short-term, hired-as-needed 

teachers for low-level courses. In my department, the TAPs are very 

much respected by colleagues and the administration. We are not 

second-class citizens, and we participate in nearly all activities. For 

better or worse, we are expected to contribute as much as TT faculty, 

and we do in the realms of our passion and expertise. I love 

my job! (Okay, it is pay raise time, and I want my department head 

to count this as one of my publications.) ■


CAREER GUIDE 

David L. Banks, 

Department of Statistical 

Science, Duke University 


This article is based on a talk I gave at the 

JSM 2007 meeting for the ASA Committee 

on Career Development. But, I should 

confess at the outset that I have no particular qualifications 

or any expertise on this topic, aside from 

having held a lot of jobs (which ought to raise 

questions about my suitability in the first place). 

Years ago, I was involved in drafting the New 

Researchers’ Survival Guide (available at www. 

imstat.org/publications). Reading over it again, we 

were awfully earnest and a bit naïve, but I think it 

has a lot of value for people who are beginning an 

academic career. So, I refer new faculty members 

to that, and, in this article, shall focus on topics 

that apply to everyone, not just recent PhDs and 

not just academics. 

Although statisticians are relatively homogeneous 

in our training, we have the usual range of 

talents, personalities, and utility functions. This 

creates many career paths and many ways to be 

successful. It also means you can be miserable if 

you get caught on a path that doesn’t fit your personal 

strengths and values.

All careers have a stochastic component, so 

we should look to dynamic programming as a 

model for continual reappraisal of our situations 

and ways that may better them. This implies a 

portfolio analysis perspective: We each have a 

different mix of strengths and weaknesses, and 

we should try to adaptively invest our energy in 

combinations that seem most likely to pay off. 

Some skills that apply to employment in all sectors 

are the following: 

Technical Strength. This is the 

foundation when you are starting out, 

but it often becomes less important as 

you advance. Especially in business and 

government, one needs breadth more 

than depth at the higher levels. 

Computational Ability. Anyone 

who can do solid statistical programming 

will never miss a meal. It is a blue 

chip skill and a way of thinking that has 

unique value. But, it is hard to become 

rich or famous on this alone. 

Public Speaking. Every member of 

the ASA has survived at least 1.5 decades 

of dull lectures in school, which is why it 

amazes me that so many of us have not 

learned enough from that experience to 

avoid giving bad talks. Good presentations 

are a key component of almost any success 

story, and whatever you can do to 

build strength in this area will repay your 

efforts. If English is not your native language, 

that can be an obstacle, but personality 

and style are much more important. 

Feng Liang of Duke University and 

Xiao-Li Meng of Harvard, whose native 

language is not English, give wonderful 

talks, and there are many others. 

Writing. It is crucial to be able to write 

clearly, correctly, and briefly. This is a 

lifelong learning process—anyone who 

writes well is constantly studying how to 

write and attending to their prose. 

Social Networking. This is crucially 

important, and it sometimes seems statisticians 

study it more, while learning less, 

than those in any other field. You need a 

diverse network; having a lot of friends 

who work on local asymptotic minimaxity 

is not as helpful as having friends with 


“ Most of us will 

hold many jobs. 

Our chief assets 

are reputation, 

capability, and 

social capital— 

not seniority. 

” 


complementary strengths. In particular, 

bring your secretary flowers, talk to the 

system administrator about D&D, and 

go to a conference where there aren’t 

many statisticians. 

Organization. This sounds mundane, 

but it is very hard for a manager to promote 

you if you are sloppy or slow about 

paperwork. And the discipline of quick 

turnaround on such items (phone calls, 

emails, appointments, referee reports) 

helps in other aspects of one’s career. 

Time Management. When I was 

a young faculty member at Carnegie 

Mellon, Jay Kadane shared with me this 

advice: Carry a pocket diary and mark 

down everything you need to do. But, also 

realize everyone goofs off some (except 

maybe Peter Hall and David Dunson). 

So, don’t waste time feeling guilty about 

wasting time—just be efficient when you 

actually get down to work. 

Someone else would probably generate a 

slightly different list, but these are all key areas 

to cultivate. 

One situation where many people damage 

their careers is at internal business meetings. 

Always be concise. Try to read the interpersonal 

dynamics—you need to hear the subtext, 

read the body language, and notice what 

doesn’t get said. Do not pursue pet projects or 

raise dead issues—the way to lobby for those is 

through one-on-one talks, and it can be a slow 

process to build support. 

When I joined the National Institute of 

Standards and Technology, Lynne Hare advised 

me to “avoid other people’s nonsense.” Help keep 

meetings focused; people loathe those who, often 

unconsciously and with benign intent, hijack a 

meeting. Probably no constructive meeting lasts 

more than 50 minutes, and no participant can 

make more than one key point per meeting (but 

you should generally try to make a point, just to 

prove you are relevant). Except when brainstorming 

over beer, you should never think out loud, 

unless you are the smartest person in the room, and 

even then it is perilous. A good rule of thumb for a 

meeting is to pretend you are one of the characters 

on the television show “West Wing.” 

Most of us will hold many jobs. Our chief assets 

are reputation, capability, and social capital—not 

seniority. So, when one moves on, try to leave only 

friends behind—it isn’t always easy, as changing jobs 

is usually prompted by dissatisfaction, but, after a 

few years, things will seem less intense. My sense is 

that no organization is more than two bad managers 

(in time or hierarchy) away from meltdown, so 

it is good to have an exit plan. And, each time you 

change jobs, you get the chance to learn new skills 

and make new friends; most of us can advance faster 

by moving than by staying. In terms of long-range 

planning, you need to think at least two moves 

ahead to achieve significant career growth, and you 

should choose those moves to ensure an ironclad 

résumé for the position you ultimately want. 

For those who need to stick in their job, there 

are still ways to advance. Personality counts for a 

lot. Try to pretend to be happy and productive. 

Read the newspaper so you have a wealth of conversation 

topics and aren’t stereotypically dull or 

narrow. You should avoid doomed projects, those 

that do not build new professional assets, and those 

for which you are not central. I’d recommend looking 

for projects that cross division boundaries—it 

helps to have a broad base of good opinion, and 

you can build unique collaborations the organization 

needs. When David Blackwell introduced 

Cuthbert Daniel to the Berkeley statistics department, 

he said “This man is worth 10 of us—not 

because he is better, but because he is different.” 

So, try to differentiate yourself. Think of at least 

one new idea a week, but be properly skeptical 

of its value.

Some people can climb high by sucking up. 

And this is not evil, nor is it uncouth if done 

with panache and a degree of dignity. It crosses 

a moral line (I think) if one takes advantage of 

such favoritism to torpedo others in the office. 

More broadly, statisticians may face special ethical 

challenges; we often have to find honest ways 

to let the data speak on behalf of our employers’ 

interests, and sometimes that just isn’t possible. In 

such cases, you must use all your powers of diplomacy 

to head off direct conflict. But, if such arise, 

then doing the right thing is not going to protect 

you. So, keep an updated résumé handy and don’t 

wait around when the weather changes. 

The ASA was created to be an engine for career 

growth for all statisticians. Among its many other 

services, it offers the following: 

Salary surveys for statisticians, broken out 

by useful covariates such as sector and rank— 

this is essential for evaluating job offers and 

negotiating raises 

Professional job fairs at every JSM—the 

last day is open to all, and it never hurts to 

look at your options and keep your search 

skills sharp 

Continuing education opportunities, both 

as a student and as a teacher 

Sections, committees, and chapters that are 

ladders for leadership within the ASA, offer 

chances to network with like-minded people; 

and help build your résumé 

The opportunity to practice public speaking 

at every JSM and a 20-minute forum at which 

to advertise yourself 

Journals, a directory of members, online 

job listings, short courses, a conference 

calendar, and other ways to keep up with 

our profession and colleagues. 

These ASA resources are a huge benefit of 

membership, and their relative advantage is 

especially great for master’s statisticians, isolated 

statisticians, and those working in industries or 

agencies whose professional staff includes many 

nonstatisticians. ■ 

CAREER GUIDE 



Top Five 

Favorites 

We asked a few statisticians what 

their top five favorite statistical 

books were and why they chose 

them as important added components 

to their particular job. Here is 

what they had to say. 

� 

Dalene Stangl 

Director, Professor of the Practice of Statistics 

and Public Policy, Duke University 

Editor, Reviews of Books and Teaching 

Materials, The American Statistician 

1. Bayesian Statistics 8, published by Oxford 

Press, edited by J. M. Bernardo, 

M. J. Bayarri, J. O. Berger, A. P. Dawid, D. 

Heckerman, A. F. M. Smith, and M. West 

2. Statistical Decision Theory and Bayesian 

Analysis by James O. Berger 

3. Statistics by David Freedman, Robert Pisani, 

and Roger Purves 

4. Theory of Statistics by M. J. Schervish 

5. Bayesian Approaches to Clinical Trials 

and Health-Care Evaluation by David J. 

Spiegelhalter, Keith R. Abrams, and 

Jonathan P. Myles

�Barry Nussbaum 

Chief Statistician, U.S. Environmental 

Protection Agency 

1. The Cartoon Guide to Statistics by Larry 

Gonick and Woollcott Smith 

Despite its name (or, perhaps because of 

it), it is a good primer on statistics. Its 

catchy illustrations and succinct prose 

provide just the explanation of statistical 

concepts a statistician needs when describing 

some basic work to nonstatistician 

decisionmakers. 

The next three are classics in terms of 

established books, which display and 

explain statistical methodology. 

2. Statistical Methods by George Snedecor 

and William Cochran 

3. Applied Regression Analysis by Norman 

Draper and Harry Smith 

4. Nonparametric Statistics for the 

Behavioral Sciences by Sidney Siegel and 

N. John Castellan Jr. 

5. Finally, I rely on an old favorite: Darrell 

Huff’s How to Lie With Statistics. Far from 

being an instruction manual for statistical 

lies, it has a wealth of illustrations 

describing how statistics can be misused. 

Frequently, in complex analyses, we fail to 

realize we are making some of the mistakes 

Huff explains so well. So, I gladly use his 

examples to explain the fallacies we may 

be getting into. 

�Julie Legler 

Director, Statistics Program, 

St. Olaf College 

1. Statistics: A Guide to the Unknown by 

Roxy Peck, George Casella, George Cobb, 

Roger Hoerl, and Deborah Nolan. I first 

became enthralled with this book many 

years ago when it was edited by Judith 

Tanur. In its current form, it is possible to 

find compelling, real-to-life examples for 

any level of cour se I am teaching. 

2. Statistical Sleuth by Fred Ramsey and 

Daniel Schafer—a difficult book for a 

first course for undergrads, but one we 

refer to often over the course of students’ 

time here. 

3. Generalized Linear Models by P. 

McCullagh and J. A. Nelder provides a 

definitive, unified, treatment of methods 

for the analysis of diverse types of data. 

I realize the following two books do not 

exactly make for leisure time reading, but 

they come in handy as references. 

4. The R Book by Michael J. Crawley 

5. Handbook for Statistics Using Stata by 

Sophia Rabe-Hesketh and Brian S. Everitt 

or A Handbook of Statistical Analyses Using 

R by Brian S. Everitt 

�Chris Olsen 

AP Statistics Teacher, 

Thomas Jefferson High School 

1. Statistics by David Freedman, Robert 

Pisani, and Roger Purves 

2. Statistics for Experimenters: An 

Introduction to Design, Data Analysis, and 

Model Building by George Box, William 

Hunter, J. Stuart Hunter, and William 

Gordon Hunter 

3. Introduction to Design and Analysis of 

Experiments by George Cobb 

4. Elementary Survey Sampling by William 

Lyman Ott, Richard Scheaffer, and 

William Mendenhall 

5. Statistics: A Guide to the Unknown by 

Roxy Peck, George Casella, George Cobb, 

Roger Hoerl, and Deborah Nolan 


A re You Media Shy… 

Media Savvy? 

or 

While many of the people I’ve 

worked with would sooner have 

several teeth pulled than do a 

media interview, others take to the process 

like a duck to water. The basic difference 

between the two attitudes seems to be the 

individual’s confidence in his or her ability 

to control the interview. They would rather 

run the other way than get involved in a 

situation where they might be misquoted or 

misrepresented. The good news, however, is 

that every media-shy person can become 

media savvy with a little knowledge and 

practice. You can learn how to be more in 

control of an interview and to understand 

what the interviewer wants and needs. 

Sir Laurence Olivier attributed his success 

to two things: the confidence to perform 

and the humility to prepare. 

Six months ago, the ASA unveiled its 

Media Experts Program, and we have had 

good feedback from the media who have 

used it. Some of our experts have had a 

great deal of media experience, and I asked 

them for their best advice on doing media 

interviews. Some of their comments are 

CAREER GUIDE 

Rosanne Desmone, 

ASA Public Relations Specialist 

quoted below, while others are incorporated 

into the text. One of the journalists who 

uses our experts list fairly frequently is Carl 

Bialik, The Wall Street Journal’s Numbers 

Guy. I asked Bialik what he was looking for 

when he spoke with an expert and included 

his answers in the sections below. Bialik also 

said he wanted an expert willing to give 

an overall assessment of the statistical relationship 

or statistical techniques used in a 

paper he’s covering, rather than addressing 

a few minor quibbles … the big picture, so 

to speak. In general, this is a good point to 

remember for every interview. 

Below are some guidelines to help you 

become a media savvy interviewee. 

Respect deadlines, but buy 

some time to think. 

The first point on Bialik’s list of what he 

looks for when he speaks to an expert is 

“awareness of the importance of deadlines.” 

That said, however, if you’re asked to do an 

interview, buy a little time. Ask what the 

editor/writer wants to discuss, and don’t 

do the interview until you can think about 



what you’re going 

to say. News people 

almost always work 

on deadlines of a few 

hours to a few days, but 

that doesn’t mean you have 

to jump in without knowing 

what will be discussed. If you 

tell the journalist you’re in the 

middle of something and can talk 

to them in 15 or 30 minutes, you are 

respecting their need for speed, and 

those few minutes will give you time 

to think about the topic before you start 

answering questions. If they have a longer 

lead time, usually they will be happy to 

schedule a time to talk later. 

“Make sure you understand what is 

being asked, and that you can contribute 

something sensible on the point,” advised 

David Peterson, who is semi-retired and 

formerly of the Duke statistics faculty. 

Remember that you may 

be talking to someone 

with little or no knowledge 

of statistics. 

There are basically two types of news people: 

generalists and specialists. The generalists 

are not subject-matter experts; they 

may cover a strike in the morning, an auto 

accident in the afternoon, and a program 

on nuclear fusion in the evening. Some 

science writers have a lot of science background. 

In both cases, however, you may 

be dealing with people who know little or 

nothing about statistics. Don’t overestimate 

the interviewer’s expertise; you may need to 

provide enough background about a subject 

so you can lead the interviewer to ask 

the right questions. As one journalist once 

aptly put it, “If I don’t ask the right question, 

make sure you give the right answer.” 

“If a reporter’s question is unclear or not 

posed in such a way that one can answer it 

usefully, the most valuable thing one can do 

is to first provide some background explanation 

to help them understand the issues. 

That is, lead the reporter to ask the right 

question, then answer it,” said Peter F. Thall 

of the M. D. Anderson Cancer Center. 

Keep it simple 

All our media experts agree you have to 

speak in plain English and not use technical 

jargon in interviews. You need to be as 

accurate and complete as possible in your 

answers, but you shouldn’t go into so much 

detail that the answer becomes confusing. 

Using language your interviewer doesn’t 

quite understand is one of the major reasons 

experts are misquoted. The more technical 

you are, the more likely you are to be 

misunderstood and/or misquoted. 

“Avoid statistical jargon. The audience 

won’t understand and may, in fact, take 

a different meaning from your intended 

message.” This is sound advice from 

C. Shane Reese of Brigham Young 

University. 

Know your audience 

Yes, you’re talking to a journalist, but you’re 

really talking to his audience. Framing 

your answers for the journalist’s audience is 

really the key to giving a good interview. 

That’s also the key to getting your comments 

used—the more relevant they are 

to the interviewer’s audience, and the better 

you are at explaining things succinctly, 

the more likely you are to be quoted—and 

quoted correctly. So, if you’re not familiar 

with the news outlet, ask who makes up the 

audience. Journalists use their audience to 

filter what you say: What is the implication 

of the particular topic to the audience? 

Does it mean an additional congressional 

district in their state? More money for a

school district? Better health 

care? If you cannot make your 

comments relevant to the audience, 

they won’t get used. 

Jessica Utts of the University of 

California-Davis, said she tries to “translate 

technical information into something the 

public can understand.” 

Use examples to clarify 

and explain. 

Our media experts have varying opinions 

about using examples or analogies in interviews, 

although most of them voted a qualified 

yes to the use of these tools. Some said 

the wrong example can seem to trivialize an 

issue that isn’t trivial, while others feel talking 

to the journalist’s audience is not much different 

than teaching statistics to nonstatistics 

majors. While journalists welcome the kinds 

of examples that help illustrate a difficult 

point, you probably have to use your judgment 

in each case. According to Bialik, he 

wants experts he interviews to have “the ability 

to put their findings in terms accessible 

to a general audience, including providing 

analogies and examples.” 

“Yes, it is helpful to use an example, 

which can clarify how the statistical issue/ 

result is directly relevant to people’s lives,” 

said David Dunson of the National Institute 

of Environmental Health Sciences. “It makes 

things more concrete.” 

If you don’t know, say so. 

If the topic of the interview is out of the 

scope of your experience or responsibility, 

your best bet is to politely decline and, if 

possible, direct the writer to someone who 

may be able to help. Once you’re into the 

interview, don’t speculate or talk beyond 

your expertise, and remember that opinions 

are not facts. Some journalists will try to get 

you to answer a question even if you don’t 

feel qualified, but the majority of good journalists 

will accept your refusal to answer. In 

fact, Bialik cited the following as a desirable 

quality in an expert: “Honesty when they 

can’t speak with expertise about the issue” 

being addressed. 

“If you’re asked a question to which you 

do not know the answer, don’t be afraid to say 

so,” advised Susan Ellenberg of the University 

of Pennsylvania School of Medicine. “No 

one is expected to know everything.” 

Don’t say anything you don’t want to see 

in print or on the 6 p.m. news, and don’t let 

interviewers put words in your mouth. 

You can’t control the interviewer’s questions, 

so focus on controlling your answers. 

Anything you say in an interview can be 

quoted, even if it originated with the interviewer. 

For example, a journalist may ask 

you if you have problems with the methodology 

used in a study. If you repeat what 

was said, or even deny it, you can be quoted 

as talking about questionable methodology. 

Instead, answer the question by making a 

positive statement about the study or some 

aspect of it. Additionally, it’s important to 

remember that the interview is not over until 

it’s over—until the phone is hung up, the 

cameras or mikes are off, or you’ve left the 

building. You have to be disciplined enough 

to not get pulled into discussion after the 

fact because it could become the story. 

“If you wouldn’t write it and sign it, 

don’t say it,” said Earl Wilson, former Red 

Sox pitcher. 

CAREER GUIDE 

Anything that can 

go wrong might. 

Finally, it’s important to know things can 

go wrong no matter how good a job you 

do. The story could turn out to be unfair 

or inaccurate. If it does, bite the bullet 

and don’t call the journalist or editor to 

complain. It’s a bad idea to get into an 

argument with someone who buys ink 

by the barrel or videotape by the mile. 

Once the journalist files her story, it 

goes into the system, to the editor, the 

copy editor, and so on. Any of those 

folks can change the story without 

checking with the journalist. The headline 

writer can change the whole focus 

of a story with the headline he writes, 

or the producer can cut the story and 

change its meaning. 

Bottom line: There are no guarantees, 

but your best chance of having a 

good outcome is to be prepared and follow 

the guidelines above. Good luck! ■ 



Up-and-Coming 

Statisticians on 

the Verge of 

Great T hings 

Statisticians work in so many fields— 

from animal health to ecology to 

agriculture to computer science— 

and, every year, an untold number make a 

name for themselves. Amstat News asked 

around, and the names of the statisticians 

below just kept coming up as those belonging 

to people on the verge of doing great 

things. Allow us to introduce some of the 

ASA’s up-and-coming statisticians.

Name a statistician you admire and 

tell us why. 

If I’m supposed to name a historical figure here, 

I would pick Fisher, but the most honest answer 

would have to be my advisor, Peter Bickel. Not 

only because of his work, but because of the great 

positive influence he’s had on so many people’s 

lives and careers. 

What do you love most about 

your profession? 

When something you’ve been struggling with 

finally works, be it a proof or a piece of code. 

When you are collaborating with someone and 

know that, together, you are doing work neither 

of you could have produced on your own. When 

a student just understood something you’ve been 

explaining and you can see it’s a fascinating revelation 

for them. Of course, all these things are not 

unique to statistics. As for statistics, itself, the fact 

that almost everyone needs it is a nice bonus. 

Age: 33 

Title: Assistant 

Professor 

Employer: 

Department of 

Statistics, University 

of Michigan 

Alma Mater: UC 

Berkeley (PhD 2002) 

Research Interests: 

Liza Levina’s interests center on inference for high-dimensional 

data, including large p small n problems, covariance estimation, 

dimension reduction, and networks. 

What do you hate most about 


The same things that everyone else does—papers 

getting rejected, particularly by incompetent referees. 

Dealing with bureaucracy. The time pressure 

of tenure track. Also, that every doctor and 

nurse I’ve ever met felt obliged to tell me how 

much they hated statistics in college. 

What was the best career advice you 

were given? 

Co-advising graduate students. We have all had 

conversations with colleagues that end in “we 

should really talk more about this,” and then 

nothing happens because no one has time. But, 

if you get a joint student, then the collaboration 

really will happen. 

What is one web site/blog you can’t 

go a day without visiting? 

None. After my daughter was born (she’s almost 

three now), my free time has become much too 

scarce to spend it on reading blogs. 

What is something your friends 

would be surprised to learn about you? 

I think they’ll be quite surprised to see me do this 

article… 


CAREER G UIDE 


Age: 42 

Title: Biostatistician 

Employer: USDA 

Agricultural Research 

Service 

Alma Mater: University 

of Louisiana at Lafayette 

What are your research interests? 

The research I do involves statistical and epidemiologic 

approaches to identify relationships among 

dietary, physiological, genetic, and behavioral characteristics 

that may be associated with overweight or 

obesity in humans. 


tell us why. 

The name of a statistician that I admire is Don 

Mercante, who is the director of the biostatistics 

program at the LSUHSC School of Public Health in 

New Orleans. While Don is not a ‘famous’ statistician, 

he helped me out tremendously when I started 

my first job as a statistician. I knew I could always 

go to Don for help with a statistical issue, or even a 

professional issue. He was (is) truly a great mentor— 

always giving good advice and making time for you, 

no matter how busy he happens to be. 



What I love most about the statistical profession is 

the wide variety of scientific disciplines to which 

I am exposed. My first love was biology, so I truly 

enjoy seeing the relevance of statistics in biological 

fields, especially nutrition and medicine. 



What I dislike most about the statistical profession 

is the degree of mistrust and suspicion investigators 

from other fields hold for statistics. I believe this simply 

results from a lack of understanding of statistics 

and the bad press to which statistics is sometimes 

subjected. 

What is the best career advice you 

were given? 

The best career advice I was given actually came 

from my brother, who is an electrical engineer. He 

told me to never sell myself short and to view a job 

interview as a two-way street. That is, I should interview 

the prospective employer as much as he/she is 

interviewing me. 

What is one web site/blog you 

can’t go a day without visiting? 

This is going to sound so nerdy, but one web site I 

cannot go a day without visiting is the SAS documentation 

web site that contains documentation 

for all their procedures. I am constantly learning 

(or relearning) the theory behind statistical methods 

simply by reading the documentation provided 

to correctly use these procedures. It’s such a great 

resource for me. 

What is something your friends would be 

surprised to learn about you? 

Something my friends might be surprised to learn 

about me is that mathematics did not always come 

easily to me. In fact, it was often the subject I struggled 

with the most in high school and college.


The focus of my research is on 

graphical models and algebraic 

statistics. Graphical models are 

multivariate statistical models 

in which observed variables 

are constrained to exhibit 

dependence patterns associated 

with a graph. My work in 

algebraic statistics is primarily 

concerned with statistical 

inference in models that have 

parameter spaces with algebraic 

structure. Many graphical 

models are, in fact, of this 

algebraic type. 

Age: 32 


Professor 

Employer: University of 

Chicago 


of Washington 


tell us why. 

Looking beyond the circle of my doctoral and postdoctoral 

advisors but keeping within my areas of 

interest, the name Steffen Lauritzen immediately 

comes to my mind. He has done groundbreaking 

work on graphical models, and his book is the standard 

reference in this area. Over the past few years, 

I have had several opportunities to be at meetings 

organized or attended by Steffen and, over and over 

again, I am impressed by his quick thinking and his 

truly constructive comments on the various speakers’ 

presentations. I also admire Steffen’s supportive 

nature and positive attitude toward younger faculty. 

He is always encouraging, and I have never 

seen him pass on judgmental views. I think I am 

not alone in having benefited and drawn motivation 

from his suggestions. 



Academic statistics is a small field when judged by 

a headcount, but it is incredibly diverse. Research 

may address purely mathematical questions, solve 

algorithmic and computational problems, or be 

concerned with applied questions. Each one of these 

three areas is challenging in its own way, and I very 

much enjoy the exchange of ideas and problems 

between the three camps of mathematical, computational, 

and applied statistics. 



I really dislike turf wars between mathematically 

minded and applied statisticians about who does 

‘good’ or ‘important’ research. I consider myself very 

fortunate to be part of the department at Chicago, 

where we have a very collegial atmosphere. Also, I do 

not generally enjoy people pushing dogmatic views 

on the different approaches to statistical inference. 

What is the best career advice you 

were given? 

I have been guided by a number of excellent advisors. 

When enrolled in an applied mathematics program 

back in Germany, it was Friedrich Pukelsheim who 

first brought my attention to statistics. During my 

PhD studies in Seattle, I received a lot of guidance 

from my advisors, Thomas Richardson and Michael 

Perlman. It was through Thomas that I met Bernd 

Sturmfels, whose advice to spend a year at Berkeley 

as a postdoc was probably the most influential professional 

advice I have received. Based on my experience, 

I would recommend doing a postdoc to any 

fresh PhD. It is a great way to finish off thesis papers 

and explore new avenues for research. 



After checking an old email account, I usually take 

a look at http://de.yahoo.com to pick up some news 

about Germany. I probably use the site mostly to 

read up on sports news. They also have a lot of livetickers 

for soccer games and, of course, they always 

cover my favorite club: FC Bayern Munich. 



I am not sure there is anything I could do to surprise 

my friends. So, I will try to surprise some strangers 

by sharing two pieces of trivia: 

I have been playing soccer since I was a little 

kid and, naturally, I racked up a number of injuries. 

Sitting at a desk most of the day doesn’t help 

with keeping in shape, and so the frequency of 

injuries has increased over the last 7–8 years. In 

fact, I am currently working from home while 

recovering from my fourth knee surgery. For several 

years, someone’s Achilles tendon has been 

playing ACL in my left knee, and, since two 

weeks ago, someone’s patella tendon serves as the 

ACL in my right knee. 

The second piece of trivia involves a prize. Very 

few will find it surprising that pronouncing my last 

name is a very painful task in virtually all of this planet’s 

languages. However, some may find it surprising 

that outside the circle of my (great-)grandparents, 

parents, and three sisters, I have never met anyone 

with my last name. This makes me wonder whether 

I am the world’s only male heir to this concatenation 

of mostly consonants. For possible resolution of this 

matter, I would like to offer a pint of beer at the next 

Joint Statistical Meetings to the first few people who 

know/have heard of a person with last name Drton 

who is not related to me. 



Age: 30 

Title: Assistant Professor 

Employer: The 

University of North 

Carolina at Chapel Hill 

Alma Mater: Nankai 

University (BS, 1999); 

The Ohio State University 

(MS, 2001; PhD, 2004) 


My research interests are primarily in the areas 

of statistical learning and data mining, as well as 

their applications in bioinformatics. In particular, 

I have been developing new statistical techniques 

for analyzing high-dimensional data and data with 

complex structure. 


tell us why. 

R. A. Fisher, who proposed and developed many 

fundamental statistical techniques and concepts, 

including maximum likelihood, analysis of variance, 

Fisher’s information, and Fisher linear discriminate 

analysis. It is impossible to be a statistician without 

being influenced by Fisher’s statistical philosophy. In 

addition to his most celebrated career in statistics, 

Fisher was also an accomplished geneticist. With 

remarkable contributions in both theoretical and 

applied statistics, Fisher is certainly a wonderful role 

model for young statisticians. 



As a faculty member, I enjoy the freedom of being 

able to work on research problems that interest me. 

I also enjoy the satisfaction of solving a challenging 

problem after great effort. 



It is difficult for me to think of anything that I really 

hate about my profession. 

What was the best career advice 

you were given? 

To form strong collaborations with peers. Although I 

enjoy doing independent research, I have greatly benefited 

from collaborations with many other people, 

both in statistics and in bioinformatics. Besides the 

values of making contacts in the profession, stimulating 

discussions with my collaborators inspire me to 

think deeply about problems and develop relevant 

new techniques, as well as identify future problems 

to work on. 



On the professional side, I visit the Google web site 

every day to find information. On the personal side, 

I frequently visit www.mitbbs.com. It is a forum for 

Chinese students and scholars to exchange information, 

news, and views about study, jobs, and everyday 

life in the United States. 



I am now comfortable with public speaking, although 

I used to be shy and sometimes tremble while speaking 

in public. I love swimming and reading Chinese 

martial arts novels.

Age: 38 

Title: Assistant Professor 

Employer: University of 

Missouri-Columbia 


of Illinois at Chicago 


My main research area is optimal design of 

experiments. Currently, I am working on optimal 

design for generalized linear models, an 

underdeveloped area. 


tell us why. 

Jack Kiefer, because of his fundamental contribution 

to the design of experiments. 



The opportunity to tackle challenging research problems 

that may make a contribution to science. 



Because of what I love most about my profession, 

when I focus on a problem, I cannot live a ‘normal’ 

life until I find an answer. 


were given? 

Work on problems that you honestly believe will 

have a big impact and don’t worry about whether 

it is the fashion today. 



Sports news web sites. 



I am doing what I love to do. But, if I could choose a 

second career, I would be a tennis player. 

CAREER GUIDE 


CAREER GUIDE 


Age: 28 


Professor of Statistics 

Employer: 

University of 

California, Berkeley 

Alma Mater: 

Indian Statistical 

Institute, Calcutta 


Probability theory, theoretical statistics, statistical 

physics. I am more comfortable with theoretical 

issues, rather than applied ones. Mathematically, 

I’m better at analytical thinking than combinatorics. 

Currently, I’m working on a number of topics 

in probability, including spin glasses, random 

matrices, and concentration of measure. 


tell us why. 

Charles Stein. I have very little understanding or 

intuition about applied statistics, and how can I 

idolize someone whose work I cannot understand 

at a deep level? So that precludes applied statisticians. 

If I have to choose a theoretical statistician, 

Charles Stein’s name is the first that comes to mind. 

First, because I am more familiar with his work 

than that of other towering figures in theoretical 

statistics. Second, because his work is extraordinarily 

deep and beautiful. Whether it had any 

significant impact on data analysis is a moot point. 



A degree of honesty and thoroughness unmatched 

by almost any other branch of the applied mathematical 

sciences. I think that’s part of our legacy, 

and I hope we don’t lose that. 



A pessimistic feeling that the honesty and thoroughness 

mentioned in the answer to the previous question 

is slowly being replaced by other, less sustainable 

ideals borrowed from other, younger disciplines. 


were given? 

“Everyone is insecure. If you don’t feel insecure, 

look at Gauss, and you will.” — From my adviser, 

Persi Diaconis 



Wikipedia. It’s addictive. 



I have only a few friends, and they know almost 

everything that there is to know about me. So, no 

big surprises. Still, here’s a small one: When I was 

young, I used to spend hours and hours trying to 

figure out how to spin a cricket ball. Never played 

cricket, though.



Math Is Music; 

(Or, Why Are There No Six-Year-Old Novelists?) 

Richard D. De Veaux, 

Williams College, and 

Paul F. Velleman, 

Cornell University 


Statistics Is Literature 

Almost 30 years ago, something happened 

that made introductory statistics harder to 

teach. Students didn’t suddenly become less 

teachable, nor did professors forget their craft. It was 

that we began to switch from teaching statistics as a 

mathematics course to teaching the art and craft of 

statistics as its own discipline. When statistics was 

viewed as a subspecialty of mathematics, students 

were taught to manipulate formulas and calculate 

the ‘correct’ answer to rote exercises. Life for the 

teacher, both as instructor and grader, was easy. 

That started changing in the early 1980s. The 

video series “Against All Odds” appeared, and David 

Moore and George McCabe published Introduction 

to the Practice of Statistics. Since then, two pioneering 

committees—one for the Mathematical Association 

of America and the ASA and one for the National 

Council of Teachers of Mathematics and the ASA 

that produced the Guidelines for Assessment and 

Instruction in Statistics Education (GAISE) Report— 

have pushed us all to change our teaching. And a 

new generation of texts has appeared following the 

advice of these reports, challenging statistics teachers 

to use this new approach. 

But why is it more difficult to teach this way? 

And why is it so important that we do? 

By comparison, let’s look at mathematics. Much 

of the beauty of mathematics stems from its axiomatic 

structure and logical development. That same 

structure facilitates—in fact dictates—the order 

in which the material is taught. It also ensures the 

course is self-contained, so there are no surprises. 

But, modern statistics courses are not like that, and 

that can frustrate students who were expecting a

math class. As a student of one of us once wrote on 

the course evaluation form, “This course should be 

more like a math course, with everything you need 

laid out beforehand.” 

Mathematics has a long history of prodigies and 

geniuses, with many of the most famous luminaries 

showing their genius at remarkably early ages. 

We’ve all heard at least one version of the famous 

story of young Carl Friedrich Gauss. A web search 

finds more than 100 retellings of the story, but 

an article by Brian Hayes in American Scientist’s 

“Gauss’s Day of Reckoning” identifies a version 

actually recounted at Gauss’ funeral. In that version, 

Gauss—age 7 and the youngest in the class— 

summed the numbers from 1 to 100 in seconds, 

wrote the answer on his slate, and then threw it 

down on the table mumbling “there it lies” in the 

local dialect. It was perhaps an hour later that the 

teacher discovered that his answer was, in fact, the 

only correct one in the room. 

Prodigies in math can develop at remarkably 

early ages because math creates its own self-consistent 

and isolated world. Pascal had worked out 

the first 23 propositions of Euclid by age 12 when 

his parents, who wanted him to concentrate on 

religion, finally relented and presented him with a 

copy of Euclid’s Elements. Galois wrote down the 

essentials of what later became Galois Theory the 

night before a fateful duel when he was 20, or so the 

legend has it. In the modern era, Norbert Weiner 

entered Tufts at age 11; Charles Pfefferman of 

Princeton was, at 22, the youngest full professor in 

American history; and Ruth Lawrence of Hebrew 

University passed her A-levels in pure math at age 

9 and became the youngest student ever to enroll at 

Oxford two years later. 

Of course, mathematics isn’t the only field 

that shows prodigies. Mozart, Schumann, and 

Mendelssohn, among others, were young musical 

prodigies. Even though his music matured, it is 

remarkable that some of the music Mozart wrote at 

age 5 is still in the repertoire. 

Also, chess prodigies continue to appear. 

Sergey Karjakin is the youngest grandmaster ever 

at 12 years, 7 months. The infamous late Bobby 

Fischer—who was youngest in 1958 when he 

became a grand master at 15 years, 6 months, and 

1 day—is now only 19th on that list. 

But there are only a few fields that develop 

prodigies, and all seem to be self-contained. For 

example, as professor of English at the University 

of Connecticut, Thomas Dulack observed, “There 

are no child prodigies in literature.” Although one 

might argue that William Cullen Bryant, Thomas 

Chatterton, H. P. Lovecraft, or Mattie Stepanek 

qualifies as a literary prodigy, that list doesn’t have 

quite the same panache as the others we’ve cited. It’s 

no easier to find prodigies in art, poetry, philosophy, 

or other endeavors that require life experience. 

What does any of this have to do with statistics 

and how can it help us understand why introductory 

statistics is so hard to teach? The challenge for 

the student (and teacher) of introductory statistics 

is that, as literature and art, navigating through 

and making sense of it requires not just rules and 

axioms, but life experience and “common sense.” 

Although working with elementary statistics 

requires some mathematical skills, we ask so much 

more of the intro stats student than is required by, 

for example, a student in his or her first calculus 

course. A student in calculus I is not asked to comment 

on whether a question makes sense, whether 

the assumptions are satisfied (e.g., Is the reservoir 

from which the water pouring really a cone?), to 

evaluate the consequences of the result, or to write 

a sentence or two to communicate the answer to 

others. But, that’s exactly what the modern intro 

stats course demands. 

The challenge we face is that, unlike calculus I, 

we have a wide variety of skills to teach, and most of 

them require judgment in addition to mathematical 

manipulation. Judgment is best taught by example 

and experience, which takes time. But, we’re supposed 

to produce a student capable of these skills 

in one term. It would be challenging enough 

to teach the definitions, formulas, and skills 

in the standard first course. To convey in 

addition the grounds for sound judgment 

is even more difficult. It should 

be no wonder that the first course in 

statistics is widely acknowledged to 

be one of the most difficult courses 

to teach in the university. 

It is not merely that we hope to 

teach judgment to sophomores; we 

are actually asking our students to 

change the way they reason about 

Carl Friedrich Gauss, a 

math prodigy at the age of 7 

Blaise Pascal was a French 

mathematician, and a 

prodigy by the age of 12. 

Évariste Galois was a French 

mathematician and prodigy 

who finished the Galois Theory 

by the time he turned 20. 


We thank the following JSM 2008 Sponsors for their fi nancial support: 


Platinum 

Gold 

Silver 

the knowledge to act

the real world. P. F. Velleman in his 2003 keynote 

address to the Beyond the Formula conference 

called the skills they must acquire the 

seven unnatural acts of statistical thinking: 

➊ 

➋ 

Think critically. 

Challenge the data’s credentials; look 

for biases and lurking variables. 

Be skeptical. 

Question authority and the current 

theory. (Well, okay, sophomores do 

find this natural.) 

➌ Think about variation, rather than 

about center. 

➍ 

➎ 

Focus on what we don’t know. 

For example, a confidence interval 

exhibits how much we don’t know 

about the parameter. 

Perfect the process. 

Our best conclusion is often a refined 

question, but that means a student 

can’t memorize the ‘answer.’ 

➏ Think about conditional 

probabilities and rare events. 

Humans just don’t do this well. Ask 

any gambler. But, without this, the 

student can’t understand a p-value. 

Embrace vague concepts. Symmetry, center, 

outlier, linear … the list of concepts fundamental 

to statistics but left without firm definitions 

is quite long. What diligent student wanting to 

learn the ‘right answer’ wouldn’t be dismayed? 

How can we help students navigate these 

woods? We don’t have definitive answers to the 

question, in spite of our more than 50 years 

(combined) teaching of introductory statistics. 

But, we’d like to identify some themes that 

might help us as a community to start a conversation 

about some of the challenges. 

We can help students by giving them a 

structure for problemsolving that incorporates 

the requirement that they exercise their 

judgment. In our books, we’ve recommended 

that students follow the steps W. E. Deming 

created more than 50 years ago in his advice 

to industry: plan, do, check, act. We’ve substituted 

communicate for act to underscore 

the importance of communicating to others 

the results we see. Students must learn to 

communicate their results in plain language, not 

only in statistical jargon. 

As GAISE emphasized, we must place more 

emphasis on the plan and communicate steps. The 

emphasis of the traditional mathematical course, 

on the do step can be largely replaced by relying 

on technology for the calculations and graphics. 

In teaching students to think through the problem, 

plan their attack, and communicate results, 

we bring students face-to-face with their real-world 

knowledge and experience—with the literature 

side of their maturing intellect. We owe them an 

acknowledgement that we’ve done this. It isn’t fair 

to emphasize the simplicity of the calculations or to 

just provide a bunch of definitions in little boxes. 

No comparative literature or philosophy teacher 

would do that, and neither should we. 

What guidance should we offer? First, we can 

note that the judgment often called for in statistics 

is one that invites students to state their personal 

views. (After all, they are the ones who must be 

95% confident in their interval.) But, we can offer 

guidance for their judgments; they must be guided 

by the ethical goal of discovering, describing, modeling, 

and understanding truth about the world. 

Second, we can remind students their introductory 

statistics course is related to every other course 




Resources 

Guidelines for Assessment and Instruction in Statistics Education 

(GAISE) Report, www.amstat.org/education/gaise 

Hayes, Brian. (2006) “Gauss’s Day of Reckoning.” American Scientist, 

94(3):200. 

Velleman, P.F. (2008) “Truth, Damn Truth, and Statistics.” Journal of 

Statistics Education, 16(2): www.amstat.org/publications/jse/v16n2/ 

velleman.html. 

Velleman, P.F. (2003) “Thinking With Data: Seven Unnatural Acts 

and Ten 400-Year-Old Aphorisms.” Keynote address to the Beyond 

the Formula conference, Rochester, New York. 

Weiss, Cindy. (2006) “New York Philharmonic Selects 

UConn Prof to Revive Concert Series.” http://advance.uconn. 

edu/2006/060424/06042412.htm 

they may study. The reason they are taking statistics 

(or perhaps, the reason it’s required) is that they 

are accumulating the kind of knowledge about the 

real world that will help them write literature and 

read philosophy, and that kind of knowledge makes 

them qualified to make statistical judgments. Of 

course, by asking students to call upon what they’ve 

learned in other courses, we are encouraging them 

to solidify their knowledge from those courses. 

Third, we must actually require students to 

demonstrate all the steps of a statistical analysis, 

from problem formulation to communicating the 

results to making real-world recommendations on 

what they find. Unfortunately, homework and 

exam problems that carry these requirements are 

harder to write and harder to grade. Training teaching 

assistants to reliably grade these efforts can be 

problematic. Moreover, many statistics instructors 

are not trained in statistics, and they, too, can find 

this approach challenging. But, the results of teaching 

a modern course reward both the student and 

teacher, in spite of its challenges. 

We should also face outward to the academic 

community. There is a widespread 

impression 

that introductory 

statistics can be taught—or even less plausible, can 

be learned—in a single term. Any objective consideration 

of the breadth and depth of the concepts 

and methods covered shows this to be absurdly 

optimistic. Yet, few academic programs require 

more than one course, and many of those that 

require two are cutting back. We need to argue as a 

discipline that an introductory statistics course must 

cover more than an introduction to inference for 

means if it is to teach the reasoning of statistics— 

and that teaching that reasoning must be its goal 

(not just teaching definitions and formulas.) But, 

a more complete course that covers techniques that 

require more than rudimentary sophistication, such 

as inference for regression and multiple regression, 

is unlikely to have time to teach judgment, planning, 

and communication. It will most likely be 

pared down to a collection of equations and rules. 

As a community, we need to make it clear that 

the subject of statistics deserves both more respect 

and more time, not because it covers so many 

methods, but because it should teach the foundations 

of reasoning when we have data. Part of the 

argument might be that, unlike students in subjects 

that exhibit prodigies, our students must summon 

their real-world knowledge to learn to think statistically. 

And, that the effort by statistics teachers and 

students will pay back correspondingly in all our 

students do. Math is sometimes said to be the language 

of science (and much social science), but statistics 

should teach students the structure for what 

it communicates. 

Is the effort to teach the modern course 

worth it? We believe it is. Rather than a collection 

of techniques or a ‘cookbook’ of situations 

and formulas, a modern course in statistics 

must teach students to reason about the 

world. Although that makes the course more 

difficult to teach and assess, it will make a difference 

in students’ lives and serve them for the 

rest of their academic careers and beyond. ■ 

Editor’s Note: This paper is based on several 

talks given by the authors at the United S tates 

Conference on Teaching Statistics (USCOTS).


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PROFESSIONAL OPPORTUNITIES 

Illinois 

■ Full-time assistant professor, tenuretrack 

position in the Department of 

Preventive Medicine, Northwestern 

University. Requires a doctoral degree in 

biostatistics or statistics for methodology 

research and epidemiologic studies in the 

area of cardiovascular disease. Excellent 

communication skills are essential. To 

apply, send CV and references to: Marie 

Lee, Department of Preventive Medicine, 

NU Feinberg School of Medicine, 680 

N. Lake Shore Suite 1102, Chicago, IL 

60611. Northwestern University is an 

affirmative action/equal opportunity 

employer. 

■ Faculty biostatistician, Department of 

Internal Medicine. PhD in biostatistics or 

related field required. Background in longitudinal 

analysis and field epidemiologic 

studies highly desirable. Excellent communication 

and computing skills required. 

Level of appointment commensurate with 

experience. Letter, curriculum vitae, three 

references to Carlos F. Mendes de Leon, 

PhD, Rush Institute for Healthy Aging, 

Rush University Medical Center, 1645 

W. Jackson Blvd., Suite 675, Chicago, IL 

60612. Rush University Medical Center 

is an EOE. 

■ Two, non-tenure-track full/associate/assistant 

professor positions in the 

Biostatistical Collaboration Center. 

Requires a doctoral degree in biostatistics/ 

statistics for collaboration, consultation, 

teaching, and research in biomedical field. 

Excellent communication skills are essential. 

At least one year post-doctoral experience. 

To apply, send CV and references 

to: Thongsy Singvongsa, Department of 

Preventive Medicine, NU Feinberg School 

of Medicine, 680 N. Lake Shore Suite 

1102, Chicago, IL 60611. Northwestern 

University is an affirmative action/equal 

opportunity employer. 



Department Head 

The University of Texas at Dallas 

Department of Mathematical Sciences 

The University of Texas at Dallas seeks a distinguished scientist to head its Department 

of Mathematical Sciences and enhance its national and international stature. A Ph.D. in 

Mathematics, Applied Mathematics, Statistics, or related fi eld is required together with a 

proven record of scholarly research commensurate with tenure at the full professor level. 

The successful candidate will have a multidisciplinary research emphasis and will present a 

vision for the future of the department and clear evidence of leadership potential. The position 

represents an unusual opportunity to shape a mathematics department for the future in an 

expanding, research-oriented university with strong science, engineering, and management 

emphases. Salary will be commensurate with qualifi cations 

and experience. 

The department maintains strong undergraduate and graduate degree programs in 

mathematics, applied mathematics, statistics, and bioinformatics and computational 

biology, and expects to add M.S. and Ph.D. degree programs in Actuarial Mathematics and 

Biostatistics in the near future. 

The university is located in the rapidly growing and increasingly cosmopolitan suburbs of 

North Dallas amid one of the largest and most vibrant concentrations of high technology, 

multi-national corporations in the nation, including a substantial energy sector. For more 

information, visit http://www.utdallas.edu. 

Inquiries may be addressed to: 

Dr. Raimund Ober, Professor, Chair of the Search Committee, ober@utdallas.edu 

Dr. Michael Baron, Professor, mbaron@utdallas.edu 

Dr. Mieczyslaw K Dabkowski, Assistant Professor, mdab@utdallas.edu 

Dr. Sam Efromovich, Endowed Professor, efrom@utdallas.edu 

Dr. Robert Hilborn, Professor & Department Head, rhilborn@utdallas.edu 

Dr. David Lewis, Senior Lecturer II, dlewis@utdallas.edu 

Dr. Viswanath Ramakrishna, Professor, vish@utdallas.edu 

Review of applications will begin November 17, 2008, and will continue until the position is 

fi lled. The successful candidate will fi ll the position effective August 2009, although other 

arrangements are negotiable. 

Candidates should submit a complete resume or curriculum vitae, a statement of research 

interests, a letter describing his/her vision for the development of the department, and the 

contact information (names, addresses, telephone numbers, and email) for fi ve professional 

references. Electronic applications may be submitted via http://go.utdallas.edu/ 

facultyjobs and are highly encouraged. The electronic process will allow candidates to 

upload documents into a secure space directly from their computers. Emailed materials 

will not be accepted. Indication of gender and ethnic origin for affi rmative action statistical 

purposes is requested as part of the application process but is not required for consideration. 

Alternatively, application materials may be mailed to: 

Academic Search #20096 

The University of Texas at Dallas 

Mail Station AD 42, Room MP 2.228 

800 West Campbell Road 

Richardson, Texas 75080-3021 

U.S.A. 

The University of Texas at Dallas is an equal opportunity/affi rmative action university and 

encourages applicants from candidates who would enhance the diversity of the 

university’s faculty and administration. 


Maryland 

■ Seeking PhD/experienced master’s 

statisticians for Center for Devices 

and Radiological Health, FDA, HHS. 

Grapple with rich array of statistical 

issues in clinical trials for new technologies, 

from LASIK and artificial hearts to 

genetic tests and robotic surgery. Review 

statistical designs/analyses issues in medical 

devices from invention to postmarket. 

Send CV to Greg Campbell, 1350 

Piccard Drive, HFZ-550, Rockville MD 

20850, greg.campbell@fda.hhs.gov. FDA 

is a smoke-free environment and an equal 

opportunity employer. Permanent residency 

required. 

Massachusetts 

■ The Center for Health Policy and 

Research at UMass Medical School seeks 

a senior biostatistician for appointment 

at the associate professor level or above. 

Responsibilities: health services/policy 

study design and consultation to faculty 

developing scientific proposals and publications. 

Qualifications: PhD in statistics 

or closely related discipline, 5+ years postdoctoral 

experience, including applied 

research design and preparation of proposals 

for competitive funding, good interpersonal 

skills. www.umassmed.edu/chpr/ 

index.aspx. University of Massachusetts 

Medical School is an AA/EOE. 

■ Massachusetts Institute of Technology 

Mathematics Department seeking to fill 

combined teaching and research positions 

at instructor, assistant professor, 

or higher levels in statistics or applied 

probability beginning September 2009. 

Appointments are mainly based on 

exceptional research qualifications. PhD 

required by employment start date. 

Submit online at www.mathjobs.org: CV, 

research description, three recommendation 

letters. Applications should be complete 

by 1/1/2009. (See full classified text 

at mathjobs for more information.). MIT 

is an equal opportunity, affirmative action 

employer.

■ Tufts University School of Dental 

Medicine seeks a doctoral-level statistician 

to collaborate with students and faculty 

on clinical and basic science research 

and teach introductory statistics. PhD or 

equivalent in statistics, biostatistics, or epidemiology; 

research experience; familiarity 

with statistical software packages (SAS 

and SPSS); teaching experience; strong 

communication skills. Contact: Paul 

Stark, MS, ScD, Director of Advanced 

and Graduate Education, paul.stark@ 

tufts.edu. Tufts University is an AA/EOE 

employer and actively seeks candidates 

from diverse backgrounds. 

■ The MIT Sloan School of Management 

seeks applicants for a tenure-track faculty 

position in statistics starting July 1, 2009. 

Applicants should possess a PhD in a relevant 

field by date of appointment. Please 

submit your curriculum vitae, relevant 

Faculty Position 

Biostatistician 

The Division of Public Health Sciences 

The Division of Public Health Sciences of the Fred Hutchinson Cancer Research Center invites 

applications for a faculty level biostatistician with an interest in the conduct and analysis of cancer 

clinical trials and associated correlative studies to work primarily with the Southwest Oncology Group 

(SWOG) Statistical Center. SWOG is an NCI-funded national research network that conducts clinical 

trials and related studies aimed at the prevention or cure of cancer. Experience with methodological 

and collaborative research in clinical trials or high dimensional data analysis (genomics, proteomics) in 

the context of clinical studies is desired. The position will be at the rank of either Assistant or Associate 

Member (equivalent, respectively, to Assistant or Associate Professor at a university). An established 

track record is required for a position at the Associate Member level. The incumbent will be expected 

to conduct an active program of independent and collaborative research pertinent to the mission of 

the Fred Hutchinson Cancer Research Center and the Division of Public Health Sciences. The Fred 

Hutchinson Cancer Research Center is a world-renowned research institution with large and active 

efforts in basic biological sciences, human biology, clinical research, epidemiology, biostatistics and 

cancer prevention research. Its mission is the elimination of cancer as a cause of human suffering and 

death. The Center conducts research of the highest standards to improve prevention and treatment of 

cancer and related diseases. The Fred Hutchinson Cancer Research Center is an equal opportunity 

employer. The Center has a culturally diverse faculty and strongly encourages applications from female 

and minority candidates. 

Deadline for applications is November 15, 2008. Please send applications including curriculum vitae, 

a letter describing research interests, and the names of four references to: 

Search Committee Chair 

Division of Public Health Sciences 

Fred Hutchinson Cancer Research Center 

1100 Fairview Avenue North 

M2-B500, Box 19024 

Seattle, WA 98109-1024 

The Fred Hutchinson Cancer Research Center is an equal opportunity employer. 

Novartis Oncology – 

Join an Industry Leader 

At Novartis Oncology, our mission is to become the world’s premier oncology company by consistently discovering, 

developing, and producing broadly available novel therapies that improve and extend the lives of cancer patients. 

Our work is about harnessing the power of the imagination. It’s about applying our pooled experience in new ways, exploring new possibilities 

and novel pathways, and fi nding better solutions for patients. Our primary goal is to research and develop new and more effective ways to treat 

cancer—in essence, to bring our vision of research to life. Our passion is about believing in our ability to change the way cancer is treated—and 

acting on this belief. Evidence of turning our thought into action can be seen in our array of innovative products designed to treat a variety of cancers. 

It is evident through our efforts in medical education, and through our unprecedented level of support, education, and fi nancial assistance 

for patients. Our portfolio provides a broad range of innovative therapies and practical solutions that enhance the lives of patients and their families. 

Our efforts to discover and develop innovative approaches for the treatment of cancer have produced breakthrough medicines such as the 

leukemia therapy Gleevec®/Glivec®; the breast cancer agent Femara® and Zometa® for the treatment of cancer-related bone complications. 

We have approximately 3800 employees and operate in 50 countries. Our main locations include our global offi ces in Florham Park, New Jersey 

and Basel, Switzerland. 

Biostatistics and Statistical Reporting opportunities 

At Novartis Oncology Global Development, we believe our people are our greatest strength. It is through their passion for our work that we can 

provide novel therapies to patients in need. Within our Global Oncology Biostatistics and Statistical Reporting organization, we have several outstanding 

opportunities available for statisticians and statistical programmers to be based in our global offi ces in New Jersey. 

Skill and Experience Requirements 

For both statisticians and statistical programmers, excellent quantitative abilities and communication skills (written, verbal and presentation) are 

a must with a high level of analytical and conceptual ability in order to provide strategic focus to projects. Candidates must have a proven track 

record of strong execution and results. Educationally, a PhD is preferred for our Biostatistics opportunities. 

For our Programming opportunities a Bachelors degree is required and an advanced degree is strongly preferred. Education and experience 

in Statistics is highly preferred. These positions require experience in the use of statistical software, particularly SAS. Experience analyzing and 

reporting clinical research data is highly desired especially experience with Oncology data. 

Email: Natalie.Wood@novartis.com Linda.Finelli@novartis.com 

Novartis Oncology 180 Park Avenue Bldg 104/2K37 Florham Park, NJ 07932 

Novartis Oncology is an EOE - MFVH 

SEPTEMBER 2008 AMSTAT NEWS 63 

PROFESSIONAL OPPORTUNITIES



publications, a statement of objectives 

and aspirations in research and education, 

information about teaching experience 

and performance, and three recommendation 

letters by December 1, 2008 to: 

stat-search@mit.edu. MIT is an equal 

opportunity employer committed to 

building a culturally diverse intellectual 

community and strongly encourages 

applications from women and minorities. 

■ Amherst College, Assistant Professor 

of Mathematics (Statistics). Tenure-track 

position in the mathematics and computer 

science department, beginning fall 

2009. PhD in statistics, biostatistics, or 

related field required with evidence of 

excellence in both teaching and research. 

Two courses per semester: statistics and 

some mathematics. See www.amherst. 

edu/~deanfac for details. Applications 

(by December 15, 2008) and inquiries 

to search@math.amherst.edu. Amherst 

College is an AA/EOE.

New Jersey 

■ Novo Nordisk Inc., U.S. affiliate of 

Novo Nordisk A/S in Copenhagen, 

Denmark, a world leader in diabetes care, 

currently has an opening for an associate 

director, biostatistics within our clinical 

development division in our corporate 

headquarters in Princeton, NJ. For more 

information or to apply online, please visit 

the careers section of our web site, www. 

novonordisk-us.com, and reference position 

number 1162BR. EOE. 

North Carolina 

■ Bioinformatician for the Section of 

Statistical Genetics and Bioinformatics, 

Biostatistical Sciences, Division of Public 

Health Sciences, Wake Forest University 

Health Sciences. Research collaboration 

in genomics, proteomics, imaging, 

bioinformatics, and related disciplines. 

Provides bioinformatics solutions and 

The Department of Biostatistics and Bioinformatics of the Rollins School of 

Public Health and Woodruff Health Sciences Center of Emory University, Atlanta, 

Georgia, seeks a proven scholar and innovative leader to chair a growing and 

dynamic department in a vibrant school and outstanding health sciences center. 

The Rollins Chair is supported by endowment funds. 

Candidates should possess a doctoral degree in an appropriate discipline; a strong 

record of academic research and teaching, particularly at the graduate level; 

a demonstrated capacity to secure external funding to support research; and a 

compelling vision for an integrated faculty and programs in biostatistics and 

bioinformatics. The candidate should have a capacity for exceptional leadership 

in an academic setting and be able to foster multidisciplinary collaboration among 

academic departments and centers both within and outside Emory University. 

The current Department consists of 33 primary and joint faculty members as well 

as 21 adjunct faculty members http://www.sph.emory.edu/bios/index.php. The 

Department has grown rapidly in recent years with active research programs in 

statistical modeling, spatial statistics, bioinformatics, imaging, survival analysis, 

misclassifi cation models and multivariate survival models as well as health 

preparedness, infectious diseases, cancer, cardiovascular and renal diseases, and 

mental health. 

DEPARTMENT CHAIR AND ENDOWED ROLLINS PROFESSOR 

DEPARTMENT OF BIOSTATISTICS AND BIOINFORMATICS 

ROLLINS SCHOOL OF PUBLIC HEALTH and WOODRUFF HEALTH SCIENCES CENTER OF 

EMORY UNIVERSITY 

The Chair will be expected to provide guidance and vision to the development of 

an expanding department at the interface of biostatistics and bioinformatics while 

performing as a scholar, a teacher in the department’s graduate academic programs, 

a faculty colleague and mentor, and a leader in the school and university. 

Within the school and the Woodruff Health Sciences Center, there are substantial 

Emory is an Equal Opportunity/Affi rmative Action Employer 

opportunities for research collaborations in clinical trials, the new Atlanta Clinical 

and Translational Sciences Institute, computational life sciences, population based 

studies, emerging technologies and university-wide initiatives in global health, 

predictive health and neurosciences. The successful candidate will be expected 

to capitalize on a wide range of collaborative opportunities at Emory and with 

nearby institutions, including the Centers for Disease Control and Prevention, The 

Georgia Institute of Technology (e.g., the Emory-Georgia Tech Nanotechnology 

Center for Personalized and Predictive Oncology) and other local universities. 

The Rollins School of Public Health is dedicated to teaching and research, 

currently employs 140 full-time faculty members and enrolls over 900 full and 

part-time graduate students in its masters and doctoral programs http://www.sph. 

emory.edu/index.php. The department offers MPH, MSPH and PhD degrees and 

a combined 5-year BS/MSPH degree with Emory College. The school is located 

on the Emory campus in close proximity to the Centers for Disease Control and 

Prevention, Emory’s Graduate Division of Biological and Biomedical Sciences, 

School of Nursing, School of Medicine, Yerkes National Primate Research Center 

and the Winship Cancer Institute and is near the American Cancer Society headquarters. 

A major research university, Emory enrolls 11,350 students in undergraduate, 

graduate and professional programs http://www.emory.edu. 

Applicants should send a letter indicating their interest and a curriculum vitae to: 

Melissa Sherrer, Rollins School of Public Health, Emory University, 1518 Clifton 

Road, N.E., Atlanta, GA 30322 USA, preferably via email to msherre@sph.emory. 

edu. Screening of applications will begin immediately and continue until the position 

is fi lled. Starting date is negotiable. Applications will be considered confi dential 

and references will not be contacted without the permission of the applicant. 





software development for publications 

and research proposals. Email CV, research 

statement, and three letters of reference to 

dbsrecruit@wfubmc.edu. Wake Forest 

University Health Sciences is an equal 

opportunity/affirmative action employer. 

Ohio 

■ Associate/full professor, statistics, Miami 

University, Oxford, Ohio. Visit www. 

muohio.edu/mathstat/stat_ad08.html for 

details. Send CV, transcripts, research 

description, teaching philosophy, and 4 

letters of recommendation to statsearch@ 

muohio.edu. At least one letter should 

discuss teaching ability and potential. 

Application screening begins 10/15/08 

and continues until the position is filled. 

Miami University is an EOE/AA employer 

with smoke-free campuses. Campus Crime 

and Safety Report—www.muohio.edu/ 

righttoknow. Hard copy upon request. 

■ Cleveland Clinic: biostatisticians, outcomes 

researchers, and statistical programmers; 

doctoral, masters, and bachelors 

levels. The clinic is a nonprofit academic 

medical center known globally as a leader 

in patient care, research, and education. 

Its Department of Quantitiative Health 

Sciences has over 90 professionals, including 

more than 50 biostatisticians and 

statistical programmers. Openings are 

posted at www.clevelandclinic.org/qhs. The 

Cleveland Clinic is an equal opportunity 

employer. 

Pennsylvania 

■ Assistant Director/Associate Director 

Biostatistics (Clinical Neuroscience). 

Wyeth Pharmaceuticals, Collegeville/ 

Philadelphia, PA. A senior biostatistician 

opportunity is available in the CNS 

group of Wyeth Pharmaceuticals, which 

has one of the most robust Alzheimer’s 

pipelines in the industry. Be responsible 

for strategy and the overall statistical 

support for key clinical projects.

Thinking of Your 

Future? 

Let the ASA help you realize 

your professional goals. 

Visit the ASA’s JobWeb—The JobWeb is a 

targeted job database and résumé-posting service 

www.amstat.org/jobweb 





Details about this position and others 

can be found at www.wyeth.com/ 

careers. (Requisition #21390). EOE, 

M/F/D/V. 

Texas 

■ University of Texas Health Science 

Center, Houston, is seeking a senior 

biostatistician. The position offers extensive 

opportunities for research leadership/collaboration 

in the Coordinating 

Center for Clinical Trials. Responsibilities 

include holding leadership positions in 

clinical trials, conducting biostatistical 

research (methodological/collaborative), 

teaching. Qualifications include earned 

doctorate in biostatistics/related field, 

commitment to teaching, evidence of 

research/recognition in biostatistics/ 

clinical trials. Contact Barry Davis, 

MD, PhD, barry.r.davis@uth.tmc.edu. 

UTHS C is an EOE. 

continued on page 71

BIOSTATISTICS and CLINICAL TRIALS 

Faculty Position 

The University of Texas School of Public Health (UTSPH) invites applications from senior investigators 

in biostatistics to fi ll a tenure track faculty position at the Associate or Full Professor level at 

the UTSPH Houston Campus in the Texas Medical Center. 

The position off ers extensive opportunities for research leadership and collaboration within 

the UTSPH Coordinating Center for Clinical Trials, which has made signifi cant contributions to 

cardiovascular disease and vision research by serving as a coordinating center for 17 nationwide 

multicenter clinical trials. Responsibilities include: holding leadership positions in clinical trials; 

conducting biostatistical research (methodological and collaborative); teaching biostatistics 

courses for students in MS, PhD, MPH, and DrPH degree programs; advising graduate students; 

and participating in community service. 

Qualifi cations include: (1) earned doctorate in biostatistics or related fi eld; (2) commitment to 

excellence in teaching and advising graduate students; (3) evidence of both methodological and 

collaborative research accomplishments in clinical trials; (4) evidence of national/international 

recognition in biostatistics and clinical trials; and (5) excellence in written and oral communication 

skills. 

Review of applications will begin immediately and continue until the position is fi lled. Academic 

rank will be determined by the qualifi cations of the candidate. Candidates should e-mail a letter 

describing their qualifi cations and interests along with their curriculum vitae, and contact information 

for three professional references to: 

Barry R. Davis, M.D., Ph.D., Chair, Biostatistics Search Committee, The University of Texas Health 

Science Center at Houston, School of Public Health, 1200 Herman Pressler, E-809, Houston, Texas 

77030, FAX: 713-500-9530, email: Barry.R.Davis@uth.tmc.edu 

The University of Texas Health Science Center at Houston is an EO/AA employer. M/F/D/V. Minorities and 

women are strongly encouraged to apply. This is a security-sensitive position and thereby subject to Texas 

Education code §51.215. A background check will be required for the fi nal candidate. 

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70 AMSTAT NEWS SEPTEMBER 2008

■ The University of Texas School of 

Public Health is seeking a director of 

the Division of Biostatistics. Leadership 

of the division requires the vision to 

develop new programs, to increase and 

complement the success of existing 

programs, and to mentor junior 

faculty and students. Candidates 

must have a PhD in biostatistics or 

statistics and have progressed to the 

rank of full professor. Information 

contact: Robert.J.Hardy@uth.tmc. 

edu. The University of Texas Health 

Science Center at Houston is an EO/ 

AA Employer. M/F/D/V. Minorities 

and women are strongly encouraged 

to apply. ■ 

Expand Your Career Horizons! 

Visit the ASA JobWeb at www. 

amstat.org/jobweb/index.cfm 

Instructor Position 

at the Virginia Commonwealth University School of Medicine Department 

of Social and Behavioral Health 

The Department of Social and Behavioral Health Virginia Commonwealth University School of 

Medicine seeks applicants for an academic position at the Instructor level in the area of Biostatistics, 

Social Epidemiology, or related fi eld. The appointee’s responsibilities will include collaborative and 

independent research related to social and behavioral sciences. 

Applicants must have graduate level training in Biostatistics, quantitative methodology and/ 

or Epidemiology, strong interest in collaborative work with faculty, and a record of peer-reviewed 

publications in applied behavioral research. Candidates should have a working knowledge of 

statistical software packages (e.g., SPSS, SAS) for data analysis and statistical modeling. Candidates 

with an interest in cancer research are highly desirable. This position will provide support, 

consultation and collaboration for researchers specializing in various aspects of social and behavioral 

health and cancer research. The candidate will provide consultation on statistical methods 

commonly used in behavioral and cancer research. The candidate will be expected to work closely 

with researchers in writing grant proposals and articles for publication. The successful candidate will 

be encouraged to develop their own research program. 

The Department of Social and Behavioral Health (http://www.behavioralhealth.vcu.edu/) at VCU 

has multiple externally funded research projects involving various aspects of health behavior and the 

social context of healthcare outcomes. Faculty in the Department of Social and Behavioral Health are 

closely linked to the Massey Cancer Center (http://www.massey.vcu.edu/) at VCU, which is one of the 

nation’s NCI-designated cancer center offering outstanding opportunities for research. 

Candidates should send a cover letter with their CV and the names of three references to: 

Chair, Instructor Search Committee, Department of Social and Behavioral Health 

Virginia Commonwealth University, McGuire Hall Annex 

1112 East Clay Street, P.O. Box 980149 

Richmond, VA 23298-0149 

Consideration of application materials will continue until the position is fi lled. 

VCU is an urban, research intensive institution with a diverse university community and 

commitment to multicultural opportunities. VCU is an equal opportunity/affi rmative action 

employer. Women, minorities, and persons with disabilities are encouraged to apply. 

Open Faculty Positions 

Biostatistics Support Unit Director and 

RAHC Biostatistics Core Director 

The Department of Epidemiology and Biostatistics at the University of Texas Health Science Center at San Antonio has two positions open. We seek a Director for 

our new Biostatistics Support Unit (BSU). The BSU provides coordinated study design and statistical support for clinical and translational research within the university 

community and with partner organizations. The BSU Director supervises and assigns projects to BSU personnel; monitors project flow and deadlines; maintains 

contact with investigators to track project progress; tracks grant reviews and funding or publication status; and oversees the collection of web-based quality evaluations 

of services. We also seek a Director for our new Biostatistics Unit that will be located in our Regional Academic Health Center (RAHC) in Harlingen, Texas. The 

Biostatistics Unit will provide coordinated study design, statistical and clinical informatics support for clinical and translational researchers in our campuses located in 

Harlingen and Edinburg, Texas in the Rio Grande Valley. The Unit Director will provide direct collaborative support for RAHC researchers. 

These positions require a DrPH, a PhD in Biostatistics or Statistics, or a PhD in a related health science discipline. We strongly prefer 5 or more years of postdoctoral 

experience in a collaborative research environment. Successful candidates are expected to have outstanding collaborative scientific research experience, as 

well as excellent verbal and written communication skills. The candidates will be expected to help expand academic mission of the Department of Epidemiology and 

Biostatistics in the School of Medicine. 

Currently, the Department of Epidemiology and Biostatistics, consisting of 29 faculty and 25 staff, is a part of several large research programs such as the CTSAfunded 

Institute for Integration of Medicine and Science (IIMS), the Cancer Therapy & Research Center, Greehey Children's Cancer Research Institute, and the 

Barshop Institute for Longevity and Aging Studies. The department’s activities span basic, translational, clinical and population-based research. Several other new 

departmental faculty will be located in Harlingen, Texas. 

The University of Texas Health Science Center at San Antonio is located in suburban Northwest San Antonio. 

Interested candidates should send a letter of interest, curriculum vitae along with names, addresses and phone numbers of three references to: 

John E. Cornell, Ph.D., Chair, Search Committee 

Department of Epidemiology and Biostatistics 

The University of Texas Health Science Center at San Antonio 

7703 Floyd Curl Drive, Mail Code 7933 

San Antonio, TX 78229-3900 

The University of Texas Health Science Center at San Antonio is an Equal Employment Opportunity/Affirmative Action Employer. 

All faculty appointments are designated as security sensitive positions. 




Associate/Assistant 

Professor of Epidemiology 

The Department of Epidemiology, Graduate School of 

Public Health, University of Pittsburgh invites applications 

for a faculty position at the Associate or Assistant 

Professor level in the Epidemiology Data Center 

(EDC). A tenure stream or non tenure stream position 

is available, with teaching required for a tenure stream 

appointment. Candidates at the Associate Professor 

level must have a record of external funding and 

peer-reviewed publications. Candidates at the Assistant 

Professor level should have demonstrated potential for 

external funding and peer-reviewed publications. The 

faculty member will be expected to develop his/her 

own research, publish in peer-reviewed journals and 

collaborate with clinical investigators. The successful 

candidate will have excellent communication skills. A 

doctoral degree in Statistics, Biostatistics, Epidemiology, 

Bioinformatics or a related fi eld, or a medical degree 

with graduate training in public health is required. All 

specialty areas will be considered, but bioinformatics, 

clinical trials, epidemiological modeling and genetics 

will be given high priority. For information about the 

Epidemiology Data Center visit http://www.edc.gsph. 

pitt.edu. Salary and rank will be commensurate with 

experience. Applications will be reviewed until position 

is fi lled. 

Applicants should send to Position #0124944, curriculum 

vitae, a letter indicating area(s) of expertise and 

a list of three references to: 

Steven Belle, PhD 

Graduate School of Public Health 

University of Pittsburgh 

504 Parran Hall 

Pittsburgh, PA 15261 

The University of Pittsburgh is an Affi rmative Action 

Equal Opportunity Employer. 


ASA’s Online 

FORUM 

is available to members. 

The forum encourages general discussion related 

to statistics. Access to the forum may be 

found in the ASA Members Only 

area at www.amstat.org/membersonly.




Professor/Associate 

Professor of Epidemiology 

(Two Vacancies) 

The Department of Epidemiology, Graduate School 

of Public Health, University of Pittsburgh invites 

applications for faculty positions at the Professor 

or Associate Professor level in The Epidemiology 

Data Center (EDC). Tenure stream or non tenure 

stream positions are available, with teaching required 

for a tenure stream appointment. Candidates must 

have a record of external funding and peer-reviewed 

publications as well as have excellent communication 

skills. Faculty members will be expected to further 

develop their own research, publish in peer-reviewed 

journals and collaborate with clinical investigators 

both nationally and within the University of Pittsburgh. 

A doctoral degree in Statistics, Biostatistics, 

Epidemiology, Bioinformatics or a related fi eld, or a 

medical degree with graduate training in public health 

is required. All specialty areas will be considered, 

but bioinformatics, clinical trials, epidemiological 

modeling and genetics will be given high priority. 

For information about the Epidemiology Data Center 

visit http://www.edc.gsph.pitt.edu. Salary and rank will be 

commensurate with experience. Applications will be 

reviewed until positions are fi lled. 

Applicants should send to Position # 0124942, 

curriculum vitae, a letter indicating area(s) of expertise 

and a list of three references to: 

Steven Belle, PhD 

Graduate School of Public Health 

University of Pittsburgh 

504 Parran Hall 

Pittsburgh, PA 15261 

The University of Pittsburgh is an Affi rmative Action 

Equal Opportunity Employer. 





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AADVERTISING DIRECTORY 

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MISC. PRODUCTS AND SERVICES 

Birkhäuser .........................................................................p. 39 

Cambridge University Press ...............................................p. 44 

CRC Press .........................................................................p. 27 

Springer.............................................................................p. 59 


astellas ...............................................................................p. 74 

Cincinnati Children’s Hospital Medical Center .................p. 64 

The Cambridge Group......................................................p. 64 

Emory University ..............................................................p. 65 

Fannie Mae .......................................................................p. 68 

Fred Hutchinson Cancer Research Center.........................p. 63 

Mayo Clinic ............................................................... p. 69, 70 

NORC ..............................................................................p. 69 

Novartis Oncology ............................................................p. 63 

RAND Corporation ..........................................................p. 65 

SAMSI ..............................................................................p. 73 

Smith Hanley ....................................................................p. 76 

SUNY Upstate Medical University ....................................p. 66 

Takeda Pharmaceutical ......................................................p. 72 

U.S. Census Bureau ..........................................................p. 75 

The University of Georgia .................................................p. 68 

University of Louisville......................................................p. 66 

University of Pittsburgh ............................................. p. 72, 75 

The University of Texas at Dallas ......................................p. 62 

The University of Texas Health Science Center .......... p. 69, 71 

The University of Utah .....................................................p. 76 

Virginia Commonwealth University ..................... p. 67, 71, 75 

Westat ...............................................................................p. 61 

SOFTWARE 

JMP, a business unit of SAS ..............................................p. 24 

MacKichan Software .........................................................p. 22 

Minitab, Inc. .............................................................. p. 40, 41 

NCSS ................................................................................p. 45 

SAS ............................................................................... cover 3 

Salford Systems .................................................................p. 42 

SPSS ............................................................................. cover 4 

StataCorp ..........................................................................p. 53 

Stat-Ease, Inc. ...................................................................p. 12 

StatPoint/statgraphics ........................................................p. 33 

StatSoft ......................................................................... cover 2 

SYSTAT ............................................................................p. 57

Mollie Orshansky - Amstat News - American Statistical Association

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