Jim Cochran

In the 13th installment of the Amstat News series of interviews with ASA presidents and executive directors, we feature a discussion with 2001 President Richard L. Scheaffer.

Q: What was your original major when you matriculated at Lycoming College? How did this start you down the path of studying to become a statistician?

A: I was always interested in mathematics, an interest enhanced by excellent high-school math teachers from whom I took three years of algebra. I can still remember the excitement that came when I first understood the logic of deductive proof in plane geometry. Desiring to follow in the footsteps of these outstanding teachers, I decided to major in mathematics. The head of the math department at Lycoming took me under her wing and told me—yes, told me—I was going to graduate school at Bucknell University, where she had already secured a scholarship for me. At Bucknell, I came under the influence of William Mendenhall, who was creating excitement for the statistics program he was developing there. I soon learned statistics offered interesting applications of mathematics—and I was hooked. One of my mentors there told me I was going on for a doctorate. What did I know?

Q: You have published highly regarded textbooks in a wide variety of areas, including survey sampling, probability, mathematical statistics, and introductory statistics. What motivated these books, and what is your philosophy on producing and maintaining successful textbooks that cover such a diverse set of topics?

A: A genuine fondness for teaching carried over from my original goal of being a high-school math teacher. At Florida, where I was now on the faculty but still under the direction of Bill Mendenhall, we decided to develop a wide range of undergraduate courses to meet the needs of various disciplines and set the stage for an undergraduate major in statistics. In those days, there were very few statistics textbooks written for undergraduates, and so we set out to develop our own class notes, many of which became textbooks. Although some of these books contain theory, a primary emphasis was always on real-world applications that demonstrated the practical uses of statistics. We focused on making the books both student and teacher friendly, realizing that not all teachers of statistics were statisticians.

Q: You were one of the developers of the Quantitative Literacy Project (QLP), so you were instrumental in establishing the basis of the data analysis component in the National Council of Teachers of Mathematics’ curriculum standards. How did the QLP come about, and how difficult was it to convince our colleagues in mathematics education of the importance of this data analysis component?

A: Almost everything that has to do with statistics education at the K–12 level in the United States has some root connection to Fred Mosteller. Among other contributions, Fred organized the ASA-NCTM Joint Committee in the 1960s—and it is still going strong—to bring a greater emphasis on statistics into school mathematics. It is not surprising, then, that some folks appointed to that committee were disciples of the Tukey approach to data analysis. By the time I was appointed to the committee in 1980, it was chaired by Jim Swift, a fantastic high-school teacher from Canada who had developed his own material for teaching data analysis to his math kids. We quickly saw that this novel work needed to be developed a little more formally for publication to wider audiences. We also saw that we needed funds to do this. To shorten a long story, we succeeded in obtaining NSF grants to develop these materials and, perhaps more importantly, to give workshops for teachers across the country. We thought the term statistics would scare off both the granting authorities and the teachers, so we decided to call it quantitative literacy.

As to support from the mathematics community, we obviously had great support among teachers through NCTM from the very start. We also had strong support from much of the mathematics education community. John Dossey, one of the leaders of that community, was also president of NCTM at the time and a strong supporter of statistics. The main objections came from the research mathematicians, who saw possible value in teaching some statistical notions somewhere in the school curriculum, but strongly objected to teaching it as part of the mathematics curriculum.

Q: The QLP began more than 30 years ago. What has been its impact over the intervening years?

A: The NCTM curriculum standards of 1989 listed data analysis as one of the main strands of the mathematics curriculum, which took courage and foresight on their part. The College Board was coming on board at about this same time, both with their own K–12 curriculum guidelines and a renewed dialogue on the possibility of developing an Advance Placement Statistics course. As to the latter, we had to answer two key questions: What is to be the content of such a course? Are there teachers already in the field who can teach it? We had ready answers: The content would be built around the Tukey model for data analysis and the cadre of teachers capable of teaching this material would come from participants in our QLP workshops. But it still took 12 years, 1985 to 1997, to get to the first AP Statistics exam. More recently, statistics viewed from this same data analysis perspective is the basis of the influential K–12 GAISE Report and has been embedded in the Common Core State Standards.

The success of the QLP approach to teaching basic statistical concepts depended not only on a data-analytic approach, but also on hands-on, active learning. Hoping to affect a similar laboratory approach to college teaching of introductory statistics, we decided to adapt and expand our QLP workshop ideas toward that effort. The result was the Activity Based Statistics book that has seen some success in increasing the use of activities in college classrooms—but not to the degree we would have liked.

Q: Were there any topics that you felt were important, but were unable to incorporate?

A: First, what do I mean by the Tukey approach to data analysis? It is more than drawing box plots. To paraphrase the master, this includes ways of planning the gathering of data, procedures for analyzing data, techniques for interpreting the results of such procedures, and the necessary machinery of mathematical statistics that apply to the latter. We tried to cover the essence of these areas, using simulation rather than mathematical theory as the essential tool for interpretation. These four areas are practically the outline for the AP Statistics course.

Speaking now for myself, I would like to see more emphasis on statistical modeling, which generally requires use of regression analysis with more than one explanatory variable. We can now do that easily with the computational power available, but it has not gotten far as the basis of even college-level introductory statistics. Relating to an earlier point, our research mathematician friends do see great value in modeling.

Q: You have put a great deal of effort into initiatives designed to increase statistical/quantitative literacy. What does your experience suggest is the greatest impediment to achieving a numerate society?

A: Math phobia emanating from poor teaching of mathematics in earlier generations, a simplistic view of mathematics as arithmetic, and a reluctance to embrace new approaches and new topics. Learn the times tables and perhaps long division the way I did and leave the rest for experts. Kids love data; adults hate data. Somewhere along the way, something crucial went wrong. My view is that an even greater emphasis on statistical thinking through real applications of interest to students will enhance both statistical and mathematical thinking.

Q: What were the most important accomplishments achieved/issues addressed by the ASA during your presidency?

A: The board was heavily involved in building issues. The Duke Street location was no longer adequate for the future envisioned for the ASA and the site originally chosen for a new building had significant problems, which never were overcome.

On the financial side, there were concerns about income loss on journals due, in part, to online publication. This led to the establishment of a committee to plan a long-term development campaign, which was derailed by building issues, but has recently gained new life.

Amid all of that, the ASA continued to expand its support of statistics education at the school and college levels. For example, the Consortium for the Advancement of Undergraduate Statistics Education arose from an ASA strategic initiative and has become the leading U.S. organization for supporting and advancing the resources, professional development, outreach, and research in undergraduate statistics education.