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.

Webinar recorded May 9, 2006 presented by Carl Lee of Central Michigan University and hosted by Jackie Miller of The Ohio State University. Do you use hands-on activities in your class? Would you be interested in using data collected by students from different classes at different institutions? Would you be interested in sharing your students' data with others? Does it take more time than you would like to spend in your class for hands-on activities? Do you have to enter the hands-on activity data yourself after the class period? If your answer to any of the above questions is "YES", then, this Real-Time Online Database approach should be beneficial to your class. In this presentation, Dr. Lee (1) introduces the real-time online database (stat.cst.cmich.edu/statact) funded by a NSF/CCL grant, (2) demonstrates how to use the real-time database to teach introductory statistics using two of the real-time activities and (3) shares with you some of the assessment activities including activity work sheets and projects.

The AIMS project developed lesson plans and activities based on innovative materials that have been produced in the past few years for introductory statistics courses. These lesson plans and student activity guides were developed to help transform an introductory statistics course into one that is aligned with the Guidelines for Assessment and Instruction in Statistics Education (GAISE) for teaching introductory statistics courses. The lessons build on implications from educational research and also involve students in small and large group discussion, computer explorations, and hands-on activities.

A cartoon that might be used in introducing scatterplots and correlation. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.

This activity illustrates the convergence of long run relative frequency to the true probability. The psychic ability of a student from the class is studied using an applet. The student is asked to repeatedly guess the outcome of a virtual coin toss. The instructor enters the student's guesses and the applet plots the percentage of correct answers versus the number of attempts. With the applet, many guesses can be entered very quickly. If the student is truly a psychic, the percentage correct will converge to a value above 0.5.

The purpose of this activity is to enhance students' understanding of various descriptive measures. In particular, by completing this hands-on activity students will experience a visual interpretation of a mean, median, outlier, and the concept of distance-to-mean.

By means of a simple story and a worksheet with questions we guide the students from research question to arriving at a conclusion. The whole process is simply reasoning, no formulas. We use the reasoning already done by the student to introduce the standard vocabulary of testing statistical hypotheses (null & alternative hypotheses, p-value, type I and type II error, significance level). Students need to be familiar with binomial distribution tables. After the ducks story is finished, the class is asked to come up with their own research question, collect the data, do the hypotheses testing and answer their own research question. The teaching material is intended to be flexible depending of the time available. Instructors can choose to do just the interactive lecture type, interactive lecture + activity, or even add the optional material.

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.

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.

Tutorial on the ANOVA test in statistics and probability, with a description, formulas, example, and a calculator applet. This is part of the larger site Virtual Statistician at http://web.mst.edu/~psyworld/virtualstat.htm