# Study Bias Issues

• ### Quote: Rumsey on Statistical Critiques

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
• ### Quote: Geier on Statistical Literacy

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.
• ### Cartoon: The Manipulator

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.
• ### Quote: Adams on Assumptions

...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.
• ### Webinar: Introducing Informal Inference Using Data-Centric Lab Exercises

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.
• ### Two Sample Tests

This lesson introduces two sample hypothesis testing for means and discusses the one-tailed and two-tailed t-tests.
• ### Polls: What Do the Numbers Tell Us?

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.
• ### Making Students Aware of Bias

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.
• ### Sampling Bias and The California Recall

This lesson deals with the statistics of political polls and ideas like sampling, bias, graphing, and measures of location. As quoted on the site, "Upon completing this lesson, students will be able to identify and differentiate between types of political samples, as well as select and use statistical and visual representations to describe a list of data. Furthermore, students will be able to identify sources of bias in samples and find ways of reducing and eliminating sampling bias." A link to a related worksheet is included.
• ### Impact!

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.