Newsletter of the Section on Statistical Education of the American Statistical Association

Message from the Chair

Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

I am very honored to serve as Chair of the Statistical Education section of ASA this year. These are very exciting times to be involved in statistics education, and our section is active with a number of initiatives. I describe some of these below, and I also highlight some related activities of other organizations. I encourage you to get involved with the ones that interest you most.

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Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

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Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

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Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

Full Scale Evaluation of an Introductory Statistics Course -- An Example

Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

Recently, I undertook an evaluation of the introductory course (Math 37) that I had been teaching at the University of the Pacific for 5 years. My goals were to examine the changes in student reactions to the course over time, to measure long-term retention of key concepts through follow-up surveys and interviews, and to obtain external reviews of student portfolios to assess my course goals and quality of student work. Here, I outline the process I undertook with the help of an Irvine Assessment Grant administered by UOP. A copy of the full report, with results, can be obtained by request. Preliminary efforts, e.g. photocopying of assignments and compilation of course evaluations, were completed during the fall semester. Interpretation of data and surveys was conducted during the Spring semester. Two students assisted throughout the project.

Longitudinal examination of student evaluations and performance
Compiling five years worth of course evaluations, I compared student opinion on several measures:

• Level of interest in statistics (1-5 score) at beginning and end of course
• Enjoyment of hands-on activities used during the course (grouped into positive, negative and lukewarm responses)
• Opinion of lab component of course (see above groupings)
• Opinion of lab and in-class activities as an aid in learning (see above groupings)
• Opinions of lab component categorized as helpful, time consuming, and negative opinion

These results allowed me to see if there was a change in student opinion over time and if adjustments made to the materials in response to student evaluations appeared to improve these assessments.

In a separate evaluation, also completed at the end of each term, students were asked several questions about the project component of the course including:

• Did you enjoy doing the project?
• Do you think the project helped you understand the material?
• Did the project increase your interest in the material?

Responses were classified as positive, negative, and lukewarm. Again, this allowed us to examine the consistency of opinions over time.

Follow-up survey of student attitudes and understanding of key statistical ideas
To see if students maintained these opinions a year after taking the course, a questionnaire was designed and sent to all students, with known addresses, who took Math 37 from me during the 1997/98 school year. We felt it would be more beneficial to hear from most of these students before possibly conducting more in-depth interviews with just a few students. The students from these three sections were asked to respond to several attitudinal questions and one content-based question. (Several of the attitudinal questions were put on the course evaluation for the Spring 99 course and the content question was also put on the final exam of all three sections of Math 37 this Spring for comparison.) Students received a $10 bookstore gift certificate for replying to the survey. A reminder postcard was sent out one week after the first mailing.

Of the 53 surveys sent out, we received 24 responses. While there was diversity of majors and semester course was taken, it is highly important to note the voluntary nature of the responses received. In an attempt to conduct some more in-depth interviews, the students could also indicate if they were willing to participate in a second survey for an additional $20. We were able to conduct such surveys with three students. The timing of the surveys at the end of the semester was a detriment to our response rates.

Content: The first question asked students to list the three most important ideas or topics of the course. This was also compared to a UOP faculty survey I had conducted earlier of what topics they hoped their students were learning in this survey course. The second question of the study survey asked which statistical tools and concepts students had used in their subsequent coursework.

Learning Aids: Students were asked which components of the course were most helpful in learning the material. They were to choose from lecture, homeworks, in-class activities, lab activities, lab write-ups, office hours, and the textbook. Students could also enter other choices on their own. Several students volunteered the term project as a valuable learning aid. This was a regrettable omission on the survey itself.

Value of Statistics: We compared some measures of student interest level in statistics for the follow-up survey and the course evaluation at the end of the Spring semester. Students were also asked to rate their level of agreement on a scale of 1-5 (with 5 presenting strongest agreement) for 2 statements: "It is important to know something about statistics" and "I suspect that I will use statistics in my chosen line of work".

Application of knowledge: Students were also given an applied contextual question on the follow-up survey and for all three sections of Math 37 Spring 1999. This question was adapted from questions 7 and 16 of the Statistical Reasoning Assessment (SRA) instrument developed by Joan Garfield and Clifford Konold (Garfield, 98):

Listed below were several possible statements concerning the results of this research. Students were asked to agree or disagree with each statement. For example:

Three students completed the full Statistical Reasoning Assessment questionnaire. Due to the nonrandom nature of the sample, it's difficult to generalize from these results. Continued use of questionnaires such as the Statistical Reasoning Assessment Tool would allow more comparisons over time, as well as to national norms.

External reviews of student portfolios: Three external evaluators from similar institutions were identified and contacted: Carolyn Pillars, Gustavus Adolphus University; Rosemary Roberts, Bowdoin College; Katherine Halverson, Smith College. A (very) modest stipend was paid to each evaluator.

During the Fall 1998 semester, copies of primary handouts and assignments were made, as well as copies of samples of student work (homeworks, quizzes, lab reports). These samples covered a wide range of student performance. Copies of all course handouts and assignments, along with a statement of goals of the course, were also made. This material was sent to the evaluators for their perusal and reflection. Several guiding questions were also submitted to help direct their review.

Observation of student interaction in classroom and lab: We attempted to track student activities during lecture and during the common hour in the computer lab. A standardized observation form was developed and student assistants recorded the activities of a random selection of students every 5 minutes. The form was divided into two subsections -- one focusing on "people interaction" and one focusing on "classroom interaction." Our goal was to classify whether students were interacting with other students and in what way, and to measure students' level of engagement during the class.

Discussion Seminar: One component of the proposed study that was not completed was a discussion seminar involving UOP professors with interests in statistics education. Such a discussion would be extraordinarily valuable for sharing ideas, examples, and concerns about the content and pedagogy of the introductory courses across campus. To be most effective, this discussion could be moderated by an external expert in statistics education.

Conclusion: This overview has demonstrated how an evaluation could be conducted to analyze time trends in student opinion and performance, and to put a more critical eye to on-going practices. While this was a very experimental study, I feel I obtained some very useful insights about my course development. The budget for this project, including student assistant, stipends for students and external reviewers but minus the campus-wide discussion group, came to less than $1500.

Statistics Education: Perusing the Past, Embracing the Present, and Charting the Future

Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

Statistics began its emergence 200 years ago to meet needs of science and government, and has enjoyed considerable success in guiding quantitative studies for 100 years. Why, then, is it not considered part of everyone's basic school education? Why is it still among the most despised of college courses? Why has its use not become epidemic in business, industry, and government? Is progress being made to remedy these situations? The answer to the last question is a qualified "yes," but much work remains if statistics is to move to the forefront of quantitative education and practice. Opportunities abound, however, and the future could be bright!

As the recent history and current trends in statistics education have their genesis in the development of the discipline of statistics over the past 200 years, it seems appropriate to review key elements in that development. Education is generally entrenched in tradition, and statistics education is no exception. The problem is that the sciences saw statistics as a useful servant to many but a key component of none, and the traditions that became entrenched had wide enough cracks to allow statistics to fall through.

From the Pharaohs to Napoleon. From the beginning of recorded history to the late eighteenth century, the developments in statistics were few and far between. There were a few glimmers of excitement, to be sure, such as censuses in the Roman Empire, John Graunt's Observations upon Bills of Mortality in 1662, John Arbuthnot's 1710 argument in favor of Divine Providence governing births, and the exciting developments in the companion field of probability. Emperors were interested in keeping track of their people, money and key events (such as wars and the flooding of the Nile) but little else in the way of quantitative assessment of the world at large. Quantification was a tremendous problem, as pointed out by historian Theodore Porter.

In short, many mathematicians earned a living working for government or business doing complicated arithmetic, and this was not all bad.

In the late 1700's, the situation began changing rapidly. It was the Age of Reason, The Enlightenment, in which great mathematicians and scientists worked together to begin developing theoretical as well as practical solutions to the problems of the day. The unifying decimal system was developed in France (and was immediately suspected by the lower classes as a government scheme that would work to their disadvantage). Nations began serious attempts to gather data on their residents (the first United States census being in 1790), an exercise that gave our field its name, and government bureaucracies were born. One of the hallmarks of this period, though, was that mathematicians still gained income by solving arithmetic problems, and so many of the great names of the day had government jobs that allowed them to lend their brilliance to the solution of everyday problems. A case in point is Laplace, "the Newton of France," who worked for the French government in various capacities (once being the Minister of the Interior) and made innovative contributions to the country's census. Data were becoming important, and great minds were thinking about how to collect and use it intelligently.

From Measurement to Statistical Science. The science that held sway above all others around 1800 was astronomy, and the great mathematicians of the day made their scientific contributions in that area. Legendre (least squares), Gauss (normal theory of errors), and Laplace (least squares and the central limit theorem) all were motivated by problems in astronomy, an appropriate science for the Age of Reason. The importance of quantification in the physical sciences is summarized succinctly in Lord Kelvin's dictum, "If you cannot measure, then your knowledge is meager and insubstantial." Serious quantitative work in psychology and the social sciences began, however, in the nineteenth century (the Age of Romance), and by the end of the century the main thrust of statistical thinking had taken hold. In the 1860's Fechner introduced paired comparison and factorial designs for experiments in sensory perception and by the 1890's Galton had laid the foundation for regression and correlation (Stigler, 1986). It took about 100 years to get from least squares to regression analysis, whereas the two topics are now hopelessly intermingled in introductory statistics books. There was, and is, quite a philosophical difference between using least squares to estimate physical parameters of the solar system on the one hand and means of conditional distributions in a theoretical probability model on the other. Statistics is, indeed, subtle!

As statistics moved from least squares to regression, what happened to the mathematicians? Some were still fascinated by statistics, such as Galton's associates Edgeworth and Karl Pearson, but most found homes in the many universities that were established around the world in the Romantic period. As educational opportunities opened up for large segments of populations, mathematicians could find positions that paid them to think about abstract research. No longer did they have to do complicated arithmetic, or statistics, to have a decent income. So, as the number of students taking mathematics courses in colleges and universities increased, the interest of the mathematicians in statistics decreased. In addition, these same mathematicians were dictating to the schools what mathematics a high school student was required to have before entering college. Statistics in the late nineteenth and early twentieth century was becoming the province of the social sciences (Florence Nightingale being one of its proponents) and, after a young geneticist named Ronald A. Fisher took a job at an English agricultural research station in 1919, the biological sciences and agriculture. At the same time, the ever-increasing demands of industry were producing new fields of engineering, which made use of statistical procedures in research and development and in maintaining quality of manufactured products. Walter Shewhart's control charts (1925) embodied the spirit of the latter. In short, statistics lost a home with the mathematicians and was now spread across the social sciences, the biological sciences, and engineering. Statistics was emerging as a science, but had a troubled childhood; many homes offered a bed, but none would support its maturing to its full potential; this boded ill for statistics education.

From Research Centers to Undergraduate Courses in Universities. The influence of agriculture and Fisher on the maturing of statistics as a discipline cannot be overstated, and this influence permeates statistics education as well. Under Fisher's influence, research centers began springing up in Commonwealth countries, notably India and Australia. These led to educating graduate students in statistics to solve applied problems and to develop new theory, as new problems were being found with great regularity. In the United States, the first graduate programs in statistics were formed in the 1930's and 1940's at land-grant universities such as Iowa State, North Carolina State, and Virginia Polytechnic Institute, followed quickly by more theoretical programs at such universities as North Carolina, Michigan, and California at Berkeley.

World War II brought statistics back into the limelight as a very useful methodology for solving problems of importance to industrial productivity and to the success of military campaigns. Not since the Enlightenment Period (the days of Laplace) had so many great minds in the mathematical and physical sciences been directed toward the solution of practical problems. After the war, many retained an interest in statistics and myriad new programs sprang up around the U.S. and around the world. Not all went under the name of "statistics" though, as new names such as operations research, industrial engineering, and management science came into being.

During this period, undergraduate courses in statistics began to appear, especially the infamous introductory course that came to be much hated by large numbers of students over many years. This course was typically taught in a mathematics department and based on an outline that was watered down from a graduate-level theory course, covering some probability and the basics of inference (hypothesis testing and confidence intervals) with little on design of studies or analyzing data. The courses on statistical methods were very often taught in the various disciplines using statistics (particularly the social sciences, business, and agriculture). Even with all the success of the discipline during the war years, statistics still had trouble finding a stable home.

From Grand Ideas to Practical Revolutions in the Schools. In the United States, every major effort at curricular reform in the twentieth century had statistics on the list of new topics for emphasis in the schools. Typical of the thinking of the early part of the century is the following 1926 statement from Herbert Slaught, an educator at the University Chicago.

A similar statement came from one of the most highly regarded university teachers of statistics in the first half of the century, Helen Walker of Columbia University.

In later years others added similar pleas for statistics in the schools. A National Research Council report of 1947 called for introducing elementary statistics into the high school curriculum " as soon as there is a sufficient supply of trained teachers" and W. Edwards Deming in 1948 pointed to the "pressing need for introducing very general courses into the high schools and more widely into in the colleges so that ... future citizens may have the valuable orientation in quantitative thinking about social affairs which statistics affords" (Dutka, 1950). By 1975 however, a National Committee on Mathematics Education reported that "While probability instruction seems to have made some progress, statistics instruction has yet to get off the ground."

Why were these grand plans for statistics never achieved? There are many reasons, of course, among them the lack of interest and preparation of the teachers and the stranglehold that mathematicians held on the mathematics curriculum in the schools. And then there was the lack of a unified voice for statistics! Schools did teach very applied topics, such as business arithmetic, for much of the century but statistics kept falling through the cracks on both the applied and theoretical sides. It took a couple of great turns of events to finally allow statistics too succeed in the schools. One was the emphasis upon data analysis and the other was the development of technology.

Tukey and Technology. John Tukey's admonishment of "Let the data speak" set the tone for a revolution in statistical thinking in the 1970's and 80's. All of a sudden it became respectable to explore data and look at modeling as an interactive process between theory and data (even though this is what good statisticians had done for years). But data exploration is not easy to do by hand, so the companion revolution in the availability of inexpensive and easy to use technology had to accompany it. We should be clear, however, about what Tukey meant by data analysis. To him, it embraces "procedures for analyzing data, techniques for interpreting the results, ways of planning and gathering data, and the machinery of mathematical statistics which apply to analyzing data." Data analysis is part art and part science, and should emphasize "the art of cookery" rather than "cookbookery" (Tukey, 1962).

"In God we trust; all others bring data." As data analysis was becoming acceptable, the idea of collecting data to make objective decisions on all sorts of problems, from the quality of manufactured products to the efficiency of an office, was sweeping much of the world. The spirit of this revolution is captured in David Moore's remark, "If you don't know what to measure, measure anyway: you'll learn what to measure" (Cobb, 1993). This transition in statistical thinking to measurement and data analysis, with the help of easy computation, led to profound changes in the introductory college courses (changes that are still going on) and to the acceptance of a strand in statistics within the K-12 mathematics curriculum.

The NCTM-ASA Joint Committee. In the mid 1980's the Joint Committee between the National Council of Teachers of Mathematics (NCTM) and ASA, organized by Fred Mosteller in 1967, developed a series of booklets on statistics for the middle school and early high school grades. The goal was to show students and teachers how an emphasis on collecting and analyzing data and on simulation of probabilistic events could motivate and illustrate much of the mathematics curriculum while, at the same time, teaching students some important statistical skills. This so called Quantitative Literacy Project (QLP) became quite successful in changing the way many teachers thought about statistics, and became the basis for the statistics strand in NCTM's Curriculum and Evaluation Standards for School Mathematics released in 1989.

These Standards became the blueprint for the revamping of mathematics curricula in most states, and has even influenced some countries outside of the U.S. The QLP and the Standards influenced other work, notably the reports of the National Research Council's Mathematical Sciences Education Board.

The statistics strand has become so widely accepted as part of the K-12 mathematics curriculum that it has become one of the areas covered in the National Assessment of Educational Progress (NAEP), the so-called Nation's report card. Mathematics on the NAEP is defined as the five areas of number sense properties, and operations; measurement; geometry and spatial sense; data analysis, statistics and probability; and algebra and functions. These NAEP definitions also formed the basis for the mathematics portion of the Third International Mathematics and Science Study (TIMSS).

Philosophy and Style of the "New" Statistics. Developed by a team of statisticians and high school teachers, the QLP attempted to capture the spirit of modern statistics and modern ideas of education by following a philosophy that emphasized understanding and communication. That philosophy is outlined in the following steps.

This philosophy is best put into classroom practice with a teaching style emphasizing a hands-on approach that engages students to DO an activity, SEE what happens, THINK about what they just saw, and then CONSOLIDATE the new information with what they have learned in the past. This style requires a laboratory in which to experiment and collect data, but the "laboratory" could be the classroom itself; it does not need to be a computer laboratory, although the use of appropriate technology is highly encouraged.

College Courses and the Advanced Placement Connection. The same philosophy and style that marks the QLP is recommended by many for the introductory college course. Geoffrey Jowett, one of the great teachers of statistics in New Zealand and England, in his 1990 address to the Third International Conference on Teaching Statistics stated that "A statistics course at a university should have as many laboratory hours as physics or chemistry." In fact, many have offered that the teaching of statistics should resemble the teaching of science more than the traditional teaching of mathematics. Many of the originators and master teachers of statistics courses in this century (William Cochran and Fred Mosteller, to name two) actually made use of laboratory activities long before the advent of computers.

Another Joint Committee, this one between the Mathematics Association of America (MAA) and ASA, put its ideas on teaching statistics, similar to those expressed above, into formal recommendations for the introductory course that have the approval of both named associations. In summary, the recommendations (Cobb, 1992) are:

There seems to be a large measure of agreement these days on what content to emphasize in introductory statistics and how to teach the course. As a result, statistics education is making some progress and the introductory course is no longer as hated as it once was (although it is still not as well loved as many would like).

The K-12 strand in statistics and the introductory college course in the subject should both be built around the spirit of modern data analysis, design of studies, measurement, and simulation, with appropriate use of technology. The strand should serve as good background for the course. Realizing the connection between these two, a group of statistics and mathematics educators thought that the college course could, perhaps, be moved into the high school curriculum for good students interested in another option in high school mathematics. The mechanism for accomplishing this in a way that would establish national standards for the high school course was the Advanced Placement program of the College Board. An AP Statistics course was finally approved and offered for the first time in 1997. In 1999 the exam for this course was given to over 25,000 students in 1,795 high schools across the country.

With a K-12 strand in statistics, an AP Statistics course, and exciting introductory courses in colleges and universities, statistics education has truly come of age. The next step is to enhance undergraduate offerings in statistics so that more college and university students have opportunities to major in the subject or to at least strengthen their backgrounds in the subject for whatever their field of choice might be. The ASA is now planning a project that will address these issues.

Before addressing the future, it seems appropriate to look once more at how the current situation in teaching statistics is connected to the past. The modern instructional methods that emphasize simulation often use randomization procedures to introduce the notion of hypothesis testing. Some may think that is a relatively new idea. Well, that is the way R. A. Fisher thought about tests of significance 75 years ago.

In other words, randomization procedures are the way to go, and they will work without any assumption of normality. The t-test is an approximation to randomization, not the other way around. If Fisher had had a workstation, the history of statistics would have been much different. The more statistics changes to a modern approach that emphasizes data, the more it seems to agree with the old masters' original thoughts on the subject.

From elementary school to graduate school, from customers to manufacturers, from sports fanatics to health food fanatics, almost everyone seems to be interested in statistics these days. As Pogo would say, "We seem to be confronted with insurmountable opportunity." In boating (an analogy Pogo would appreciate) prudent navigation requires charting a course. Sometimes, however, the channels seen on a map are not open for efficient service because they need to be cleared of debris, widened, or deepened. The same can be said for the channels to be used in charting the course of the future of statistics education.

Clearing Channels of Communication. "Sampling is guessing," says a prominent Senator. "Sampling is no science," says a prominent newspaper columnist. There are, to be sure, legitimate scientific reasons to criticize any particular sampling plan put forth to adjust the Census, but a blanket condemnation of sampling as an invalid scientific procedure shows that there is something fouling up communication channels. "Racial Discrimination and Blood Pressure" is the title of a research article purporting to show that high blood pressure in blacks is caused by discrimination (Satel, 1997). The only problem is that the data do not show this; the social agenda of the researcher got confounded with the science. Communication problem? The recent publicity on the uses of statistical procedures to improve quality of products and services in business and industry (TQM) might lead one to believe that almost all companies use these procedures. Research shows, however, that quality concepts and tools are used extensively by fewer than half of Fortune 500 companies (Lackritz, 1997). Something is not being communicated clearly. A chemist was heard to remark, "We have discovered neural networks and no longer need statistics." This scientist needs to communicate with someone about both neural networks and statistics.

What is the debris that is blocking the communication channels? Some of it emanates from uncommitted leaders with limited understanding of quantitative issues in business, industry, government (B/I/G) and education. Some comes from a public that is easily swayed by the most recent alarming anecdote. Some comes from a workforce surrounded by technology but, at the same time, afraid of technology and easily swayed by black-box magic. Teachers at all levels need to work on clearing communication channels, even though they are sometimes caught in the middle with little support from either educational leadership or the public. Communication on statistical issues must be improved, if not with this generation of leaders, than with the next, ... or the next.

Broadening Channels of Application. Improved communication among B/I/G and educational systems at both the school and college levels will require identification of the strengths that unite and the gaps that divide. The principle uniting strength is data -- its collection and use to solve real problems. Schoolteachers must be armed with examples that not only motivate students but also convince administrators and school boards that statistics is a valuable and necessary component of the curriculum. While more independent in their decisions about courses and content, colleges and universities need to adjust their offerings to capture the interest of high school students with some statistical experience and to prepare those students for their academic and career goals. B/I/G must help provide motivating and convincing examples of the uses of statistics and work with colleges on improving course content so that college graduates, in whatever field, have an understanding of statistical thinking. All of this communication must take place in a spirit of collegiality and cooperation.

One desirable outcome of the improved communication is to have students at all levels see statistics broadly. This broad view, which must be emphasized in all courses that deal with statistical issues, can be approached by viewing statistics in three inter-related components.

Graduates of high school or college are expected to be able to read literature related to their personal life or job, understand what they read, and then use what they have learned to make decisions. (That may be what intelligence is all about.) Why should we expect less of them when the "literature" involves data?

Deepening Channels of Content. With statistics courses built around study design, data collection, and data analysis, and with the availability of appropriate technology, serious questions arise as to how content should change to enhance statistical thinking and understanding of concepts over rote use of standard procedures. Those issues are much too complex to fully address here, but a few suggestions will be offered.

Should students be exposed to statistical techniques for which they cannot understand (or even see) the derivation or computations? Some say "no" and use this as an argument against introducing transformations, logistic regression, smoothing and density estimation, and other modern topics in the introductory courses. With modern technology which allows many numerical examples to be seen quickly, it is time to rethink this position. Perhaps we should tell students about statistics the way it is practiced, not the way it is stated in textbooks. Wouldn't that help communication up and down the line?

Statistics has it roots in many fields; there is strength in diversity.
Statistics was built on real measurement problems; utility is still its greatest asset.
Statistics is dynamic; that's exciting!!!

References

• Box, Joan Fisher (1978), The Life of a Scientist, New York: John Wiley and Sons.
• Cobb, George (1992), "Teaching Statistics," Heeding the Call for Change: Suggestions for Curricular Action, Washington, DC: Mathematical Association of America, 3-43.
• Cobb, George W. (1993), "Reconsidering Statistics Education: A National Science Foundation Conference," Journal of Statistics Education, 1, 63-83.
• Dutka, S. and F. Kafka (1950), "Statistical Training Below the College Level," The American Statistician, February, 6.
• Lackritz, James (1997), "TQM Within Fortune 500 Corporations," Quality Progress, February, 69-72.
• Mathematical Sciences Education Board (1990), Reshaping School Mathematics, Washington, DC: National Research Council, 46.
• National Council of Teachers of Mathematics (1989), Curriculum and Evaluation Standards for School Mathematics, Reston, VA: NCTM.
• Satel, Sally (1997), "Race for the Cure," The New Republic, February 17, 12.
• Slaught, Herbert E. (1926) First Yearbook, A General Survey of Progress in the Last Twenty-Five Years, Reston, VA: National Council of Teachers of Mathematics, 192.
• Steen, L. A. Ed. (1997), Why Numbers Count, New York: The College Board.
• Stigler, Stephen (1986), The History of Statistics: The Measurement of Uncertainty before 1900, Cambridge, MA: Harvard University Press.
• Tukey, John (1962), "The Future of Data Analysis," Annals of Mathematical Statistics, 33, 1-67.
• Walker, Helen (1931) Sixth Yearbook, Mathematics in Modern Life, Reston, VA: National Council of Teachers of Mathematics, 111.

Interview with Robin Lock

Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

How many statistics instructors learn that their former students have applied their statistical skills to earn over $100,000 playing a lottery game? This happened to Robin Lock, Professor of Mathematics at St. Lawrence University in New York. Lock's former student and a friend applied their knowledge of probability in figuring out that the expected value of a Quick Draw lottery game at a local restaurant was greater than $0 during a special promotion.

According to Lock, these students "first raised enough cash to start play with little chance of going bust before the law of large numbers took effect to assure their expected winnings." They computed the probabilities and expectations by hand, then simulated the game many times on a computer to confirm the long run behavior. Putting the theory into practice for the remaining three days of the promotion netted the pair a profit of more than $100,000, matching almost exactly what the theory had predicted. Lock noted "Not only did they understand the application of mathematical expectation to this problem, but they had confidence in what they learned and the free time to sit all day in the restaurant playing the game." After hearing about these students' success, Lock invited them to visit his class and share the information about how they worked out the expected value, simulated the game, and decided how much to gamble.

Report on Statistical Education Program for JSM 2001

Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

The 2001 Joint Statistical Meetings will be held in Atlanta on August 5-9. The overall program theme is "Statistical Science for the Information Age." Perhaps a fitting title for our section program offerings is "Statistical Education for the Information Age." There will be a record number (four sponsored and three cosponsored) of invited sessions dealing with statistical education. These sessions will outline new program initiatives, present ways to teach new methodology, and discuss the use of modern technologies in teaching statistics in the information age of the 21^st century. Details of these sessions and of other related program items follow.

One of our allocated invited sessions is an invited panel organized by Allan Rossman. The title is "Implementing the USEI Guidelines for Undergraduate Programs in Statistics", and the panelists are Gale Rex Bryce, Brad Hartlaub, Roxy Peck and Thaddeus Tarpey. Among other things, the panel will describe how various schools have successfully implemented the USEI guidelines, and provide advice for those aiming to develop undergraduate programs at their own schools.

Another of our allocated invited sessions is organized by Katherine Halvorsen and is entitled "Nuts and Bolts: Teaching Modern Topics." Three nontraditional topics that might be included in an introductory mathematical statistics course will be presented, with suggestions on how to incorporate them into the syllabus. The topics and their presenters are "Teaching Permutation Tests and the Bootstrap" by Jenny Baglivo, "Teaching Logistic Regression" by Donald Bentley, and "A Case Study for Introducing Bayesian Methods" by Dalene Stangl.

The third of our allocated invited sessions is "Innovation and Technology in Teaching Undergraduate Mathematical Statistics," and is organized by Jay Devore. Recently, there has been a ground swell of interest in employing innovative teaching techniques and technology in introductory statistics with no calculus prerequisite. This session will focus on ways to bring the first mathematical statistics course into the 21^st century. The individual presentations with their speakers are "Using Open Source Software to Teach Mathematical Statistics" by Doug Bates, "An Interactive Environment for Learning Mathematical Statistics" by Deborah Nolan, and "S-Plus and Math Stat: Examples, Challenges and Benefits" by Andrew Schaffner. Dennis Wackerly will be a discussant for the session.

The section was successful in receiving a fourth invited session through program committee competition. This session is organized by Lynne Hare, of Nabisco, Inc., on behalf of the Statistics Partnerships among Academe, Industry, and Government (SPAIG) Committee of ASA. The session title is "Benefits of Academic and Industry/Government Collaboration." The session consists of three case studies, together with information on how to start such collaborative projects. The case studies are "SPAIG Initiative at Iowa State" by Dean Isaacson and Lonnie Vance, "Undergraduate Statistics Internships at a Major Health Research Clinic" by Lara Wolfson and Ralph O'Brien, and "JPSM -- A Government Partnership for an Academic Program" by Robert Groves and Cynthia Clark.

One of the cosponsored sessions is "Some Current Research in Statistics Learning K-12" organized by George Cobb representing AERA, with Kathleen Metz, Kay McClain and Patrick Thompson as participants. The session will feature research targeted at the elementary, middle and high school levels, with discussion by Jeff Witmer, a college teacher of statistics. Another session is "Quantitative Literacy: Success Stories and the Role of ASA Chapters" organized by Ken Newman of the ASA Council of Chapters. Chris Franklin and Dick Scheaffer will be among the participants. Papers on case studies on implementing Quantitative Literacy and Data Driven Mathematics in the public schools will be presented. The third cosponsored session is "Teaching Statistics Using Sports" organized by Jim Albert on behalf of the Section on Statistics in Sports. The participants, including Shane Reese, Jerome Reiter, Joe Gallian, and Jim Albert, will present their experiences in teaching statistics using sports.

A number of topic contributed paper sessions of great interest will also be presented. One of these is entitled "Web-Based Instruction and Distance Education: Boom or Bust." The session is organized by Mike Speed, with James Hardin, David Lane, James Davenport and Webster West as participants. Another is a panel session of AP Statistics teachers, which will be organized this year by Chris Franklin. This has always been a lively and interesting session. Other topic contributed sessions are in preparation.

In summary, there will be lots of sessions on statistical education. The program is outstanding in both quality and quantity, with a breadth seemingly sufficient to cover every section member's special interests. Let me remind everybody that February 1 is the deadline to submit contributed papers. I am so excited about the program that I am already checking out travel plans. Hope to see you there, to interact together concerning statistical education in the Information Age.

Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

Statisticians Support MAA Curricular Reform

Newsletter for the Section on Statistical Education
Volume 7, Number 1 (Winter 2001)

The Mathematical Association of America (MAA) is undertaking a major curricular review which will result in undergraduate curricular recommendations to the profession in the near future. As part of this process, the MAA asked a diverse collection of client disciplines to conduct workshops to discuss the client's needs of the undergraduate mathematics curriculum, especially the first two years. This series of Curriculum Foundations Workshops was sponsored by MAA's CRAFTY subcommittee; CRAFTY stands for Calculus Reform and the First Two Years.

Grinnell College hosted such a Curriculum Foundations Workshop for statistics on October 12-15, 2000. The ASA provided major funding for this workshop through its ASA Member Initiatives program. This funding paid travel expenses for 27 statisticians and mathematicians representing a wide variety of academic institutions as well as business, industry, and government. Roxy Peck, Allan Rossman, and I organized the workshop. Only 5 of those attending were mathematicians, who were there as observers and as resources.

In framing the agenda for this meeting, we settled on two sets of questions. The first set (called the CRAFTY Questions) was a standard set of questions provided to each of the workshops. This set of questions can be summarized succinctly as: "What should the undergraduate mathematics curriculum (esp. first two years) look like to serve the needs of statistics? Consider this question from the perspective of skills, concepts, topics, technology, pedagogy, and methods of assessment."

A second set of questions was proposed by the workshop organizers and concerned the place of statistics within a mathematics curriculum. We developed this second set based upon our understanding that statistics is a partner discipline as well as a client discipline of mathematics. By this we mean that statistics is a part of the mathematical sciences and should be represented within the curricular recommendations of the MAA. At most undergraduate institutions, there is no separate statistics department, so that responsibility for statistics offerings typically falls to the mathematics department.