Statistical Inference & Techniques

  • A cartoon to be used for discussing the selection of the best explanatory variable in a regression model. The cartoon was used in the March 2017 CAUSE Cartoon Caption Contest. The winning caption was submitted by Michael Posner, from Villanova University. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. A second winning entry, by Michele Balik-Meisner, a student at North Carolina State University, may be found at www.causeweb.org/cause/resources/fun/cartoons/variable-wheel-i Three honorable mentions that rose to the top of the judging in the March competition included "No no no! You randomize AFTER you select your research topic!" by Mickey Dunlap from University of Georgia; "This isn't what I meant by random variable!" by Larry Lesser from The University of Texas at El Paso; and "We find this method of finding 'significant' predictors to be quicker than using stepwise regression and it is even slightly more reproducible." by Greg Snow from Brigham Young University.

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  • A cartoon to be used for discussing the affect on inference caused by subject-to-subject variability and how that relates to the differences between groups. The cartoon was used in the May 2017 CAUSE Cartoon Caption Contest. This caption was submitted by Larry Lesser from The University of Texas at El Paso and took honorable mention in the contest. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. The winning caption in the May competition may be found at www.causeweb.org/cause/resources/fun/cartoons/product-testing-i (written by Jim Alloway of EMSQ Associates) and an honorable mention may be found at www.causeweb.org/cause/resources/fun/cartoons/product-testing-iii written by John Bailer from Miami University.
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  • A song to aid in the discussion of the meaning and interpretation of p-values and type I errors. The song's lyrics were written in 2017 by Lawrence Lesser from The University of Texas at El Paso and may be sung to the tune of the 1977 Bee Gees Grammy winning hit "Stayin' Alive." The audio recording was produced by Nicolas Acedo with vocals by Erika Araujo, both students in the Commercial Music Program at The University of Texas at El Paso.

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  • This is a complete lesson module (including example problems with answers to selected problems) for the purpose of enabling students to: 1) Provide examples demonstrating how the margin of error, effect size, and variability of the outcome affect sample size computations. 2) Compute the sample size required to estimate population parameters with precision. 3) Interpret statistical power in tests of hypothesis. 4) Compute the sample size required to ensure high power when hypothesis testing.
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  • When performing a hypothesis test about the population mean, a possible reason for the failure of rejection of the null hypothesis is that there's an insufficient sample size to achieve a powerful test. Using a small data set, Minitab is used to check for normality of the data, to perform a 1-Sample t test, and to compute Power and Sample Size for 1-Sample t.

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  • Document (pdf) illustrating a test of normality using an Anderson-Darling test in MINITAB and a test of equality of variances with an F-test in EXCEL.
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  • Powerpoint explaining what power is and how power and sample size are related to one another.
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  • A resource providing information about what the sample size is, what factors the sample size depends on, and how it can be determined,
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  • Resource providing information about: computation of the sample size and the assumptions that must be made to do so. Several examples are given with different conditions in each, and a table showing minimum sample sizes for a two-sided test.
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  • Article that explains why comparing statistical significance, sample size and expected effects are important before constructing and experiment.
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