SIGMAA Stat-Ed
Mathematical Association of America
Newsletter 2
February, 2004
Contents:
News from the Joint Math Meetings, Phoenix, Jan 7-10




SIGMAA Stat-Ed Business Meeting
SIGMAA Stat-Ed Sponsored Sessions
Other Stat-Ed related presentations
MAA-AMS Joint Committee Meeting
Upcoming Events
SIGMAA Stat-Ed Business Meeting
The business meeting for the SIGMAA was held Thursday evening. Secretary Julie
Clark has distributed full minutes. The new officers were introduced:
Chair:
John McKenzie, Babson College, mekenzie@babson.edu
Chair-Elect: Tom Moore, Grinnell College, mooret@grinnell.edu
Treasurer:
Chris Lacke, Rowan University, lacke@rowan.edu
Carolyn Cuff moves to Past-Chair; Julie Clark (Hollins University, jclark@hollins.edu)
continues as Treasurer for another year.
Numerous upcoming events were announced (see more on these below).
There was some discussion on the charter, proposals to amend it has been made by
Nkechi Agwu. It was decided that the executive committee would check the charter carefully
this year both for our purposes and for some changes coming from MAA and forward any
proposals to the membership.
An informal (non-scientific) poll of attendees present at the time was taken on the
question “What do you allow students to use for tests – notecards, open book, etc?” The answers
were
1 – nothing
1 – notecard
10 – open book
6 – instructor supplies formulas
1 – notebook of “10 points” – there are 40 points on each test.
1 – lets class discuss what’s important during review, and leaves it on the board
1 – anything except a computer
1 – test has two parts – take-home (anything allowed), in class (nothing allowed)
2 – 8-1/2 by 11 “notecard”
SIGMAA Stat-Ed Sponsored Sessions
The SIGMAA sponsored two sessions at the meetings. The first was a panel discussion
Thursday afternoon. The topic for discussion was “The undergraduate mathematical statistics
sequence. Allan Rossman (Ca Poly, San Luis Obispo) was moderator; the panelists were
Matthew Hassett (ASA, AdvancePCS) who presented the views of a working actuary, Elliot
Tanis (Hope College), and Deborah Nolan (UC Berkeley). The discussion basically revolved
around three issues:
1. What’s good about the sequence?
2. What’s bad about the sequence, and
3. Recommendations for change.
The general consensus was that although the sequence has problems and could stand some
revisions we still need it. Some suggested that students should have an applied course before
tackling the mathematical version.
The SIGMAA also sponsored a contributed paper session “Discourse on Inference” on
Friday morning. There were 12 presentations, which were very good. Attendance at the session
was good; however in informal polling we found that well over half of the attendees were not
members of the SIGMAA! Membership forms were distributed to those interested.
A short summary of each presentation is below. There is a second attachment to this email with the abstract for each talk. For more information, please contact the presenter.
“Why Do We Check Assumptions for Inference?” – Allan Rossman and Beth Chance
(Cal Poly, San Luis Obispo)
Allan and Beth presented JAVA applets to demonstrate coverage rates for confidence
intervals in situations where the assumptions are not met. Those can be found on their website,
www.rossmanchance.com. Allan also mentioned the case of using inference improperly – a
sample of 100 people (US Senators) found 14 women and 86 men. Based on this sample a 95%
confidence interval for the proportion of women in the population is 7.2% to 20.8%. BUT – the
sample is not random; furthermore, students do not need a confidence interval to estimate the
proportion of women in the Senate!
“Developing and Understanding Confidence Intervals for Proportions” – Daniel Teague,
NC School of Science and Mathematics, (teague@ncssm.edu).
Dan presented some ideas related to activities he used to demonstrate confidence
intervals in his classes, including the “human confidence interval” idea. Students line up as
possible proportions (each representing 1/n of the class). For various sample sizes and observed
proportions, they “step forward” if their personal proportion is reasonable for the population.
They can visually see how the intervals behave.
“Order Statistics, Uniform Distributions, and Confidence Intervals” – Michael Ratliff,
Northern Arizona University, (Michael.Ratliff@nau.edu).
Michael discussed finding confidence intervals using order statistics where one has a
sample from U[0,b] where the objective is to find the shortest interval to estimate the unknown
maximum b. He uses Mathematica in his class with graphical approaches, the linear simplex
method, etc.
“Question based presentation of inferential methods” – Michael Landringham, Idaho
State University, (landmich@isu.edu).
He asks questions leading up to the idea of a confidence interval (and the fact that one
doesn’t need to know μ. For example, what is the chance x-bar is within $0.40 (or $0.50) of the
population mean if μ = $8.50 (or $9.30) and σ = $2.10, etc. The point is to get students to realize
the probability depends on σ, not on μ.
“What does a P-value mean?” – Patricia Humphrey, Georgia Southern University,
(phumphre@georgiasouthern.edu).
This is a low-tech way to demonstrate p-values to a class based on the idea of an altered
deck of cards. To answer the question “Is the deck fair?” each student draws a card and the
number of reds and blacks is tallied. Simulation with a TI-83 calculator approximates the
probability of the observed count to approximate a p-value.
“Hands-on experiments in inferential statistics” – Mohammed Salmassi, Framingham
State College, (msalmas@frc.mass.edu).
He uses several ideas from the Schaeffer, et al book in his class. Some examples are
weighing small boxes of raisins, using the penny age an random rectangles to develop
confidence intervals and hypothesis tests.
“Using Simulations to Teach the Meaning of the P-value.” – Murray Siegel, Sam
Houston State University, (mth_mhs@shsu.edu).
He discussed two simulations. The first involved a company that is hiring 30 technicians.
Forty percent of the qualified applicants are female; only eight are hired. Is this evidence of
hiring bias? What is the probability, based on simulation of eight or fewer females being hired?
The second involved selection of one’s favorite cable network from choices CNN, ESPN, HBO
and QVC. This is used to illustrate the Chi-squared distribution (with null hypotheses equal
likelihood).
“Using Visual Tools in the Teaching of Hypothesis Testing” – Chris Lacke, Rowan
University, (lacke@rowan.edu).
Chris uses JMP and descriptive statistics and graphs. He also demonstrated JMP’s ability
to do p-value and power animations.
“Using Simulations, Permutations and Randomizations to help Students Understand Pvalues” – Robin Lock, St. Lawrence University, (rlock@stlawu.edu).
Robin used an example relating the payrolls of major league baseball teams to ask if the
payroll for those who make the playoffs significantly higher than those who don’t. He
demonstrated simulations using a deck of 30 cards (each with a team payroll and drawing eight
card to represent playoff teams), and also using Fathom for more intensive simulation. Fathom
was also used to scramble the available data to ask whether the correlation between salary and
number of wins was positive (it was for the 2003 season).
“Teaching Statistical Inference to In-service Middle School Teachers: Methodologies,
Activities, and Projects” – Barbara Wainwright, Salisbury University,
(bawainwright@salisbury.edu).
Barbara works with middle school teachers and shared some strategies she uses as well as
some projects actually done by students in her class.
“An Interdisciplinary Approach to Teaching Statistics to Undergraduate Life Science
Majors Provides Numerous Opportunities for Data Analysis Projects Involving Inference” –
Deborah Lurie, Saint Joseph’s University, (lurie@sju.edu).
Deborah and her co-authors have collaborated with the Biology department to develop a
course (with NSF funding). The Intro Statistics course is taught concurrently with the first
Cellular Biology course. The two share data analysis projects (data collected in the Biology lab
is analyzed in Statistics), and the analysis reported back to the Biology lab. At the end of the
semester, each student designs an experiment of their own an analyzes the results which are
communicated with a poster presentation.
“Hypothesis Test Selection: A Practical Method.” – Sue Shou, Idaho State University,
(shousue@isu.edu)
Sue presented a series of questions students should ask themselves when trying to
determine which is the proper test to consider for a situation.

Other Stat-Ed related presentations
Perhaps most notable in this category was Ann Watkins’ presentation as retiring MAA past
president, “Fallacies in Elementary Statistics,” which included some subtle differences
between the mean, median, and mode. “Which average would you use to answer the
following question: “What speed are you going if the number of cars you pass is equal to the
number that pass you?” The text answer was median; she used an example to shoe that the
mean is the correct average. She also explored the idea of bimodality – heights of men and
women. How far do two distributions have to be from one another for a mixture to appear
bimodal? Helguerro (1904) said two standard deviations, she said really 3. BUT –
bimodality can happen by chance – she gave an example from simulation.
Ann also addressed the California K-12 standards and said they are not rigorous with
respect to statistics (and contain a fair bit of nonsense); not surprising when no statistician
was involved!
She went on to discuss the “average class size” fallacy and said there were two points of
view and two sets of numbers: administration and students and they are different.
Administration says average class size is 12, but students sit in classes of 200!
Other statistics related presentations (I give the title, presenter, title and e-mail contact for
most as we all know it’s impossible to attend all the talks!) were submitted to other sessions
or were not included in the SIGMAA’s session. Abstracts can be found by presenter’s name
on the AMS website at www.ams.org/amsmtgs/2078_presenters.html.
My apologies to anyone’s presentation that may have been missed.
“Loading the Bases: Teaching a Statistics Course With the WWW, The Smart Board,
and Baseball” – Michael Huber, USMA, (am6996@usma.edu)
“The Missing Chapter in Elementary Statistics Textbooks: A Prescription for Transition
into Statistical Inference” – Gerald Agbegha, Johnson C. Smith University,
{gagbegha@jcsu.edu).
“A Probability Class Report” – Jose Sanqui, Appalachian State University,
sanquijat@appstate.edu)
“Papers and Peer Review in Advanced Statistics” – Kelly Cline, Carroll College,
(kcline@carroll.edu). Kelly uses a semester project in her calculus-based class, and has
students in groups of 6 each “peer review” 6 papers from the class, in which they are forced
to rank the papers 1-6 along with giving suggestions for improvement to each. After the
review, students are allowed to revise their papers with an explanation of each change.
“Teaching a Summer Elementary Statistics Course Online” – Hasan Hamdan, James
Madison University, (hamdanhx@jmu.edu)
“Our Favorite Level Curves and Linear Regression Demos” – Agnes Rash, St. Joseph’s
University, (arash@stu.edu)
“Using a POP quiz to Introduce Statistics to Weary” – Jane Wilburne, Shippensburg
University, jmwilb@ship.edu). Jane uses a pop quiz (literally about old pop music) in an
introductory class. Half of the class are told the quiz counts, half are told it will not count for
them. The data collected from quiz results are used later in the course.
“Using Sport to Teach Mathematics and Statistics” – Reza Noubary, Bloomsburg
University, (rnoubary@bloomu.edu)
“The Use of Sports Data in an Introductory Applied Statistics Course” – John McKenzie,
Babson College, (mckenzie@babson.edu)
“Examples of the Application of Statistics and Probability to Compute Science” – Juana
Sanchez, UCLA, jsanchez@stat.ucla.edu)
“What will it take to convince you?” – Mary Sullivan, Rhode Island College,
(mmsullivan@ric.edu). Mary presented a good version of the analogy between statistical
hypothesis tests and the American jury trial.
“A Demonstration of Teaching and Applying Bayes’ Formula” – Morteza ShaffiMousavi, Indiana University South Bend, (mshafi@iusb.edu). His classes actually do a team
project consulting for local entities such as the school district and IUSB. He presented
results from IUSB where student retention was studied as it related to many factors (major,
age group, SAT scores, etc.)

MAA-AMS Joint Committee Meeting
The Joint Committee met Thursday morning. Discussion topics included a report on the state
of the SIGMAA from Carolyn Cuff (574 members, which includes those joining for the first
time at no cost).
CAUSE – the Consortium for Undergraduate Statistics Education.
Activities were reported, including the plan for a US-COTS to be held in May 2005 at the
Ohio State University. CAUSE has also received an NSF grant to support a segment of the
National Science Digital Library. They are looking for submissions and reviewers. Contact
Dennis Pearl of Ohio State if you’re interested.
TEAMS – Conference on Teacher Education, Assessment, Methods and Strategies
This received funding from NSF and ASA. Eighteen teams made up of statisticians, high
school teachers, math educators and administrators took part in a conference at the University
of Georgia, Fall 2003. A report will be posted on the ASA website.
GAISE – Guidelines for Assessment and Instruction in Statistics Education
This is a panel working to revise the Cobb (1992) recommendations on statistics education.
A writing meeting will take place at ASA in February finalize recommendations. These
recommendations are seen as more prescriptive for the introductory statistics course than the
USEI guidelines. For more information, contact Robin Lock (rlock@stlawu.edu) for more
information.
MAA Prep Workshops
The committee recommended workshops in the future focus on such topics as sampling,
forecasting, computer intensive methods, categorical data analysis and Bayesian statistics.
They also cautioned that software used in the workshops be easily accessible to participants
after the course, which had not always been the case.
Upcoming Events
Events and deadlines for future planning.
ISCAT Immersion Workshop – June 21-25, 2004, San Luis Obispo CA
Led by Allan Rossman and Beth Chance, this NSF-funded workshop will demonstrate and
discuss activities for post-calculus introductory statistics courses. DEADLINE FOR
APPLICATION: February 15.
www.rossmanchance.com/iscat for more information.
ARTIST – Assessment Resource Tools for Improving Statistics Education – August 1-4,
2004
Organized by Joan Garfield, Beth Chance and Joy Jordan, this NSF-funded course will focus
on designing and implementing assessment programs, writing and grading exams, and
incorporation of alternative assessment methods. DEADLINE FOR APPLICATION:
March 1.
www.gen.umn.edu/artist/ for more information
2004 Meetings:
ASA – Toronto, August 8-12.
Beyond the Formula – Rochester NY, just before the JSM
ICME10 – Copenhagen Denmark in July
Second NCTM Yearbook on Teaching Statistics will be issued this year. DEADLINE FOR
SUBMISSIONS: March
2005 Meetings
US-COTS –Columbus Ohio, May 19-21. Deb Rumsey of the Ohio State University is the
program chair.
JMM – the Joint Math meetings will be in Atlanta, January 5-8.
2006 Meetings
ICOTS-7 in Salvadore Brazil, July 2-9. Preliminary information can be found at
www.maths.otago.ac.nz/icots7/layout.html.
JMM – San Antonio, January 12-15.