Resource Library

February 12, 2008 Teaching and Learning webinar presented by Christopher J. Malone, Winona State University and hosted by Jackie Miller, The Ohio State University. The procedural steps involved in completing a statistical investigation are often discussed in an introductory statistics course. For example, students usually gain knowledge about developing an appropriate research question, performing appropriate descriptive and graphical summaries, completing the necessary inferential procedures, and communicating the results of such an analysis. The traditional sequencing of topics in an introductory course places statistical inference near the end. As a result, students have limited opportunities to perform a complete statistical investigation. In this webinar, Dr. Malone proposes a new sequencing of topics that may enhance students' ability to perform a complete statistical investigation from beginning to end.

January 8, 2008 Teaching and Learning webinar presented by Dennis Pearl, The Ohio State University and hosted by Jackie Miller, The Ohio State University. This presentation describes the "Buffet" method for teaching multi-section courses. In this method, students are offered a choice of content delivery strategies designed to match different individual learning styles. The choice is exercised through an on-line "contract" entered into by students at the beginning of the term. The webinar describes the Ohio State experiences with the buffet strategy and discusses how key elements of the strategy can also be adapted to smaller classes to improve student learning.

November 13, 2007 Teaching and Learning webinar presented by Michael Rodriguez and Andrew Zieffler, University of Minnesota, ad hosted by Jackie Miller, The Ohio State University. This webinar includes an introduction to the idea of assessment for learning - assessments that support learning, enhance learning, and provides additional learning opportunities that support instruction. Several fundamental measurement tools are described to support the development of effective assessments that work.

July 10, 2007 Teaching & Learning Webinar presented by Larry Lesser, University of Texas at El Paso, and hosted by Jackie Miler, The Ohio State University. Drawing from (and expanding upon) his article in the March 2007 Journal of Statistics Education, Larry Lesser discusses and invite discussion about examples, resources and pedagogy associated with this meaningful way of engaging students in the statistics classroom.

April 10, 2007 webinar presented by Maria C. Pruchnicki, The Ohio State University, and hosted by Jackie Miller, The Ohio State University. Distance education and online learning opportunities, collectively known as "e-learning", are becoming increasingly used in higher education. Nationally, online enrollment increased to 3.2 million students in 2005, compared to 2.3 million in 2004. Furthermore, nearly 60% of higher education institutions identify e-learning as part of their long-term education strategy. Newer educational technologies including course management systems and Internet-based conferencing software can be used to both deliver content and engage participants as part of a social learning community. However, even experienced faculty can face pedagogical and operational challenges as they transition to the online environment. This interactive presentation discusses a systematic approach to developing web-based instruction, with an Ohio State University experience as a case example.

The International Statistical Literacy Project (ISLP) puts out a newsletter bimonthly. According to ISLP, "The mission of the International Statistical Literacy Project (ISLP) is to support, create and participate in statistical literacy activities and promotion around the world." This newsletter is a way to get information out to those interested.

In this hands-on activity, students count the number of chips in cookies in order to carry out an independent samples t-test to see if Chips AhoyŒ¬ cookies have a higher, lower, or different mean number of chips per cookie than a supermarket brand. First there is a class discussion that can include concepts about random samples, independence of samples, recently covered tests, comparing two parameters with null and alternative hypotheses, what it means to be a chip in a cookie, how to break up the cookies to count chips, and of course a class consensus on the hypotheses to be tested. Second the students count the number of chips in a one cookie from each brand, and report their observations to the instructor. Third, the instructor develops the independent sample t-test statistic. Fourth, the students carry out (individually or as a class) the hypothesis test, checking the assumptions on sample-size/population-shape.

This hands-on activity is appropriate for a lab or discussion section for an introductory statistics class, with 8 to 40 students. Each student performs a binomial experiment and computes a confidence interval for the true binomial probability. Teams of four students combine their results into one confidence interval, then the entire class combines results into one confidence interval. Results are displayed graphically on an overhead transparency, much like confidence intervals would be displayed in a meta-analysis. Results are discussed and generalized to larger issues about estimating binomial proportions/probabilities.

This activity allows students to explore the relationship between sample size and the variability of the sampling distribution of the mean. Students use a Java applet to specify the shape of the "parent" distribution and two sample sizes. The simulation then samples from the parent distribution to approximate the sampling distributions for the two sample sizes. Students can see both sampling distributions at the same time making them easy to compare. The activity also allows students to determine the probability of extreme sample means for the different sample sizes so that they can discover that small sample sizes are much more likely than large samples to produce extreme values. Keywords: sampling distribution, sample size, simulation

This lecture example discusses calculating chance with probabilities (a ratio of occurrence to the whole) or odds (a ratio of occurrence to nonoccurrence). It presents a clinical example of measuring the chance of initiating breastfeeding among 1000 new mothers. Tables are provided in pdf format.