Resource Library

A quote to initiate a discussion about critiquing statistical issues in public policy statements seen in the media. The quote is from American writer and public policy researcher Kathleen Geier (1963 - ) and may be found in her article "On the importance of statistical literacy," in Washington Monthly May 12, 2012.

A quote to motivate discussions of the importance of statistics for critical thinking. The quote is by Deborah J. Rumsey (1961 - ), The Ohio State University. The quote appears in Chapter 1 page 10 of her book, Statistics For Dummies 2nd edition, 2011

January 11, 2011 T&L webinar presented by Rakhee Patel(University of California - Los Angeles, UCLA) and hosted by Jackie Miller (The Ohio State University). Since formal hypothesis testing and inference methods can be a challenging topic for students to tackle, introducing informal inference early in a course is a useful way of helping students understand the concept of a null distribution and how to make decisions about whether to reject it. We will present two computer labs, both using Fathom, that illustrate these concepts using permutation in a setting where students will be answering interesting investigative questions with real data.

...the most misleading assumptions are the ones you don't even know you're making is a quote by English author Douglas Noel Adams (1952-2001) that can be used in teaching the importance of understanding the assumptions being made that underlie statistical inference. The quote is from the 1990 book "Last Chance to See" that was co-written with Mark Carwardine. It is part of a passage that Adams wrote about his experience watching a silverback gorilla in Zaire and trying to imagine what the animal was thinking about him.

A cartoon for use in discussions about how to critique quantitative evidence presented in the media. The cartoon is the work of Theresa McCracken and appears as #7203 on McHumor.com Free for non-profit use in statistics course such as in lectures and course websites.

This lesson introduces two sample hypothesis testing for means and discusses the one-tailed and two-tailed t-tests.

This tutorial opens with a survey on polling. Upon completing the survey, students are taken through an election example which uses polling to explain random sampling, bias, margin of error, and confidence intervals.

The following exercise can illustrate the problem of bias in estimators to students in statistics courses. In some advanced courses an alternative estimator may be presented and properties of this estimator may be investigated via Monte Carlo studies.

This Java applet tutorial prompts the user to input the components of a hypothesis test for the mean. Hints are provided whenever the user enters an incorrect value. Once the steps are completed and the user has chosen the correct conclusion for accepting or rejecting the null hypothesis, a statement summarizing the conclusion is displayed. The applet is supported by an explanation of the steps in hypothesis testing and a description of one-tailed and two-tailed tests.

This is an exercise in interpreting data that is generated by a phenomenon that causes the data to become biased. You are presented with the end product of this series of events. The craters occur in size classes that are color-coded. After generating the series of impacts, it becomes your assigned task to figure out how many impact craters correspond to each of the size class categories.