Resource Library

In this video (which lasts a little over 21 minutes), Oxford mathematician Peter Donnelly reveals the common mistakes humans make in interpreting statistics -- and the devastating impact these errors can have on the outcome of criminal trials.

Ellen Gundlach and Nancy Palaez (both of Purdue University) use Calibrated Peer Review, an online writing and peer evaluation program available from UCLA, to introduce statistical literacy to Nancy's freshman biology students and to bring a real-world context to statistical concepts for Ellen's introductory statistics classes in an NSF-funded project. CPR allows instructors in large classes to give their students frequent writing assignments without a heavy grading burden. Ellen and Nancy have their students read research journal articles on interesting subjects and use guiding questions to evaluate these articles for statistical content, experimental design features, and ethical concerns.

Statistics educators are keenly aware of the value of using real data to help students see the relevance and applicability of statistics. The federal statistical agencies have invested in significant efforts to make data accessible and available. In this webinar, Ron Wasserstein will point you to these resources, discussing their uses and limitations.

Certitude is not the test of certainty. We have been cock-sure of many things that were not so. is a quote of American Supreme Court Justice Oliver Wendell Holmes, Jr. (1841 - 1935). The quote is found in an article written by Justice Holmes in 1918 for the "Harvard Law Review" v. 32, page 40. The quote is also found in the book "Statistically Speaking, a Dictionary of Quotations" by Carl Gaither and Alma Cavazos-Gaither.

October 14, 2008 Teaching and Learning webinar presented by Daniel Kaplan, Macalester College and hosted by Jackie Miller, The Ohio State University. George Cobb describes the core logic of statistical inference in terms of the three Rs: Randomize, Repeat, Reject. Note that all three Rs involve process or action. Teaching this core logic is more effective when students are able to carry out these actions on real data. This webinar shows how to use computers effectively with introductory-level students to teach them the three Rs of inference. This is done with another R: the statistical software package. The simulations that are carried out involve constructing confidence intervals, demonstrating the idea of "coverage," hypothesis testing, and confounding and covariation.

November 18, 2008 Teaching and Learning webinar presented by Xiao-Li Meng, Harvard University and hosted by Jackie Miller, The Ohio State University. Statistics 105 is a team-designed course that has received local media attention (e.g., www.news.harvard.edu/gazette/2008/02.14/11-stats.html). Its course description promises the following: Discover an appreciation of statistical principles and reasoning via "Real-Life Modules" that can make you rich or poor (financial investments), loved or lonely (on-line dating), healthy or ill (clinical trials), satisfied or frustrated (chocolate/wine tasting) and more. Guaranteed to bring happiness (or misery) both to students who have never taken a previous statistics course, and to those who have taken statistics and want to see how statistical thinking applies to so many areas of life. This webinar reveals its history, pedagogical motivation, innovations, and challenges along the way

webinar illustrates how personal response systems (clickers) can be used to address the realization of these three recommendations in large lecture classes (over 70 students). The session discusses general issues of the implementation of clickers and then provides an example of each of the following three uses of clickers in the classroom: 1) questions designed to highlight common conceptual misunderstandings in statistics, 2) questions designed as review questions for topics already addressed, and 3) questions that were part of a class activity to help students learn a concept.

December 9, 2008 Teaching and Learning webinar presented by John H. Walker, California Polytechnic State University and hosted by Jackie Miller, The Ohio State University. Ethics play an important role in statistical practice. How can we educate our students about statistical ethics--especially when our courses are already packed with so much...statistics? At the Joint Statistical Meetings in August, 2008 Dr. Walker was the discussant in a session on "Teaching Ethics in Statistics Class." The webinar first briefly reviews the points raised by the speakers in that session. George McCabe (Purdue) contrasted the "old" introductory statistics course with its emphasis on methodology to the "new" course. Patricia Humphrey (Georgia Southern) spoke about how she covers ethical data collection in her introductory classes. Paul Velleman (Cornell) talked about the role of judgment in statistical model building and how it makes students (and sometimes us) uncomfortable. The webinar presentation discusses each of these points in the context of the American Statistical Association's "Ethical Guidelines for Statistical Practice" as well as discussing experiences in teaching statistical ethics in an undergraduate capstone course for statistics majors. It closes with an example of statistical ethics in the use of multiple comparison procedures.

April 14, 2009 Teaching and Learning webinar presented by Beth Chance and Allan Rossman, Cal Poly, and John Holcomb, Cleveland State University, and hosted by Jackie Miller, The Ohio State University. This webinar presents ideas and activities for helping students to learn fundamental concepts of statistical inference with a randomization-based curriculum rather than normal-based inference. The webinar proposes that this approach leads to deeper conceptual understanding, makes a clear connection between study design and scope of conclusions, and provides a powerful and generalizable analysis framework. During this webinar arguments are presented in favor of such a curriculum, demonstrate some activities through which students can investigate these concepts, highlights some difficulties with implementing this approach, and discusses ideas for assessing student understanding of inference concepts and randomization procedures.

June 9, 2009 Teaching and Learning webinar presented by Dalene Stangl, Duke University, and hosted by Jackie Miller, The Oho State University. This webinar presents the core materials used at Duke University to teach Bayesian inference in undergraduate service courses geared toward social science, natural science, pre-med, and/or pre-law students. During the semester this material is presented after completing all chapters of the book Statistics by Freedman, Pisani, and Purves. It uses math at the level of basic algebra.