Study Bias Issues

  • A song that can be used in discussing lurking variables - unobserved variables that may drive the relationships seen in the data. The lyrics were written by Mary McLellan from Aledo High School in Aledo, Texas as one of several dozen songs created for her AP statistics course. The song may be sung to the tune of the 2013 song “Happy,” written by Pharrell Williams for the animated movie Despicable Me 2. Also, an accompanying video may be found at
    https://www.youtube.com/watch?v=iWf-8_UjUyg

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  • A song that may be used in discussing how confounding variables may provide alternate explanations for the data making causal interpretations difficult. The lyrics were written by Mary McLellan from Aledo High School in Aledo, Texas as one of several dozen songs created for her AP statistics course. The song may be sung to the tune of the mid-20th century folk song 99 Bottles of Beer. Also, an accompanying video may be found at
    https://www.youtube.com/watch?v=-daUPdUV8C4

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  • A game for use in the active learning of linear regression and sampling biases. TigerSAMPLING is almost identical to TigerSTAT. However in the TigerSAMPLING game there are additional questions that emphasize BIAS and GENERALIZABILITY. These games collect data and explore models for estimating the age of a Siberian tiger. In this game, students act as researchers on a national preserve where they are expected to catch tigers, collect data, analyze their data (using the simple linear regression on transformed data), and draw appropriate conclusions. The TigetSTAT labs handouts were created by Rod Sturdivant (Ohio State University), Kevin Cummiskey (West Point) and John Jackson (West Point). Tietronix Software developed the game. This resource is part of the Stat2Labs collection.
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  • 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
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  • 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.
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  • 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.
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  • ...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.
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  • 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.
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  • This lesson introduces two sample hypothesis testing for means and discusses the one-tailed and two-tailed t-tests.
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  • 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.
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