chapter 9 history and teaching statistics · 2017-08-25 · chapter 9 history and teaching...

Chapter 9History and Teaching Statistics

Deborah Nolan

When Terry Speed arrived in Berkeley in the 1980s, I too was a new arrival. He wascoming to Berkeley as a senior hire and I as a junior. It was through our connec-tion to David Pollard that we discovered our mutual interest in teaching statistics.We first collaborated on a small project to introduce computing into the advancedundergraduate theoretical statistics course. The computing exercises we developedwere aimed at students uncovering, through simulation studies, some of the rulesof thumb that a practicing statistician regularly uses. This was not as successful aswe had hoped because our students didn’t see any reason to care about the simula-tion results. We had fallen into the trap Terry warns against in Speed [10]: teachingpseudo-applied statistics with context-free numbers. Subsequent attempts led us toconnect the work to real applications and then to the template described in Nolanand Speed [8] and used in Stat Labs: Mathematical Statistics through Applications[9]. It would seem that this template should have been an immediate and obvious re-sult of Speed [10]. It wasn’t. While Terry modestly claims to be “no exception – forallowing ourselves to forget the fundamental importance of the interplay of ques-tions, answers and statistics”, I dare conjecture that one of his goals in the projectwas for me to gain experience through trial and error in developing an effectiveapproach to teaching statistics.

Speed [10] successfully argues that the “whole point of statistics lies in the in-terplay between context and statistics.” Others share this viewpoint as noted in thequotes included in the article from James, Cox, Dawid, and Tukey. However, Terrytakes this assertion into the education arena and compels us to reflect this importantthesis in our teaching. Following Speed [10], others have made similar argumentsto change statistics education. According to Cobb and Moore [3], “The focus onvariability naturally gives statistics a particular content that sets it apart from math-ematics itself and from other mathematical sciences, but there is more than justcontent that distinguishes statistical thinking from mathematics. Statistics requiresa different kind of thinking, because data are not just numbers, they are numbers

D. NolanDepartment of Statistics, University of California, Berkeleye-mail: [email protected]

S. Dudoit (ed.), Selected Works of Terry Speed, Selected Works in Probability and Statistics,DOI 10.1007/978-1-4614-1347-9 9,

377© Springer Science+Business Media, LLC 2012

378 D. Nolan

with a context.” Higgins [6] and Nicholls [7] echo these statements; e.g., Higginsclaims that “for the past 40 years, statistics has been doing a great job of trainingtheoretical statisticians, but we have a more data based society and it is crucial thatwe identify changes to course content and delivery that need to occur.” Similarly,Wild and Pfannkuch [11] note “the biggest holes in our educational fabric, limitingthe ability of graduates to apply statistics, occur where methodology meets context(i.e. the real world).”

One teaching strategy offered in Speed [10] is to meet people with data by, for ex-ample, pairing the statistics teacher with a teacher in an empirical field of inquiry orpairing statistics students with students who have subject matter knowledge. Anec-dotal evidence of the success of this approach appears in Field et al. [5]. There welearn of the preparation of Betty Allan, Mildred Barnard, and Helen Turner for suc-cessful careers in biometrics at CSIR in the 1930s. All three women spent significanttime in Rothamsted Station where they learned statistics by designing and carryingout experiments under the guidance of Fisher, Wishart, and Yates.

Terry raised and answered in his 1986 paper two common objections to workingwith real problems: that these problems are too complex and the data too large tobe practical in the classroom and that only the most advanced students who have asufficiently large set of tools can successfully attack real problems. Today, we facea new version of these same concerns. Data are now free and ubiquitous. Peoplewith all sorts of backgrounds have ready access to data. This data explosion is anenormous opportunity for us to make better, more informed decisions. However,this opportunity presents challenges because people expect to be able to interactwith data in new ways and the role of the statistician is changing.

As I reflected on Terry’s call to change how we teach statistics, it was at firstdisconcerting to see that we are still asking statistics educators to consider this issue.Cobb [2] explains that “What we teach was developed a little at a time, for reasonsthat had a lot to do with the need to use available theory to handle problems that wereessentially computational.” Efron [4] describes the mathematical statistics course as“caught in a time warp” that “does not attempt to teach what we do and certainly notwhy we do it.” Brown and Kass [1] examine statistics graduate training and warnus to break away from the view of the statistician’s role as “short-term consultant”because that model “relegates the statistician to a subsidiary position, and suggeststhat applied statistics consists of handling well-formulated questions, so as to matchan accepted method to nearly any kind of data.” I have since realized that we mustperiodically revisit this question of how best to teach statistics and that is preciselythe point. We are not aiming at a fixed target that once arrived at we will haveaccomplished our goal.

Efron [4] suggests starting over by imagining “a universe where computing pre-ceded mathematics in the development of statistics” and advocates “starting moremuscularly without worrying about logical order of presentation” and focusing in-stead on the basic kinds of reasoning and explanations that can be arrived at throughrandomization-based inference. Cobb [2] further develops this notion, explaininghow randomization-based inference “makes a direct connection between data pro-duction and the logic of inference that deserves to be at the core of every intro-

9 History and Teaching Statistics 379

ductory course.” Cobb further posits that “Technology allows us to do more withless: more ideas, less technique. We need to recognize that the computer revolu-tion in statistics education is far from over.” Brown and Kass [1] advocate takinga “less restrictive view of what constitutes statistical training.” They see a blur-ring of the distinction between people with data and people with statistical exper-tise and state “some of the most innovative and important new techniques in dataanalysis have come from researchers who would not identify themselves as statisti-cians.” Brown and Kass recommend we minimize prerequisites to research, requirereal-world problem solving in our courses, and embrace a deeper commitment tocross-disciplinary training. Efron [4], Cobb [2], and Brown and Kass [1] advocatetwenty-first century changes to statistics education that echo Terry’s call to includethe value of statistics in our training programs.

Terry Speed’s advice from twenty-five years ago remains extremely relevant to-day as computational and data challenges continue to evolve and shape our field.

References

[1] E. Brown and R. Kass. What is statistics? Am. Stat., 63:105–110, 2009.[2] G. Cobb. The introductory statistics course: A Ptolemaic curriculum? Technol-

ogy Innovations in Statistics Education, 1:1–15, 2007.[3] G. Cobb and D. S. Moore. Mathematics, statistics, and teaching. Am. Math.

Mon., 104:801–823, 1997.[4] B. Efron. Is the math stat course obsolete? In Panel Discussion at the

Joint Statistics Meeting, San Francisco, CA, 2003. www.amstat.org/sections/educ/MathStatObsolete.pdf.

[5] J. B. F. Field, F. E. Speed, T. P. Speed, and J. M. Williams. Biometrics in theCSIR: 1930–1940. Aust. J. Stat., 30B(1):54–76, 1988.

[6] J. J. Higgins. Nonmathematical statistics: A new direction for the undergradu-ate discipline. Am. Stat., 53:1–6, 1999.

[7] D. F. Nicholls. Future directions for the teaching and training of statistics atthe tertiary level. Int. Stat. Rev., 69:11–15, 2001.

[8] D. Nolan and T. P. Speed. Teaching statistics: Theory through applications.Am. Stat., 53(4):370–375, 1999.

[9] D. Nolan and T. P. Speed. Stat Labs: Mathematical Statistics Through Appli-cations. Springer Texts in Statistics. Springer-Verlag, New York, 2000.

[10] T. P. Speed. Questions, answers and statistics. In R. Davidson and J. Swift,editors, Proceedings: The Second International Conference on TeachingStatistics, pages 18–28, Victoria, BC, Canada, 1986. University of Victoria.

[11] C. Wild and M. Pfannkuch. Statistical thinking in empirical enquiry. Int. Stat.Rev., 67:223–248, 1999.

www.amstat.org/sections/educ/MathStatObsolete.pdf

www.amstat.org/sections/educ/MathStatObsolete.pdf

380 9 History and Teaching Statistics

chapter 9 history and teaching statistics · 2017-08-25 · chapter 9 history and teaching...

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