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  • A cartoon to be used for discussing the importance of efficiency in sampling. The cartoon was used in the April 2017 CAUSE Cartoon Caption Contest. The winning caption was submitted by Mickey Dunlap from University of Georgia. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. Three honorable mentions that rose to the top of the judging in the April competition included “Better to ask for help BEFORE you're drowning in data!,” written by Larry Lesser from University of Texas at El Paso; “I guess I should have asked for more details before signing up for this "Streaming Data" workshop,” written by Chris Lacke from Rowan University; and “On reflection, random sampling WITH replacement might not have been appropriate in this scenario,” written by Aaron Profitt from God’s Bible School and College.
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  • A cartoon to be used for discussing the value of stratification in reducing the variability of population estimates (and the difficulty in doing so when the population weights are unknown).. The cartoon was used in the May 2017 CAUSE Cartoon Caption Contest. The winning caption was submitted by Jim Alloway of EMSQ Associates. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. Two honorable mentions that rose to the top of the judging in the May competition may be found at https://www.causeweb.org/cause/resources/fun/cartoons/product-testing-ii written by Larry Lesser from University of Texas at El Paso and at https://www.causeweb.org/cause/resources/fun/cartoons/product-testing-iii written by John Bailer from Miami 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 cartoon to be used for discussing the advantages and disadvantages of random assignment when n is small and there are clear confounders (here the assignment might be to which product is tested first). The cartoon was used in the May 2017 CAUSE Cartoon Caption Contest. This caption was written by John Bailer from Miami University 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-ii written by Larry Lesser from The University of Texas at El Paso.
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  • A joke to help in recalling the purpose of Correlation and Regression. The joke was written in 2017 by Dennis Pearl from Penn State 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."
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  • A poem to develop an understanding of permutations. A question like "Why is the word importunate used in a poem about a permutation?" will help the conversation. The poem was written by Larry Lesser from The University of Texas at El Paso in 2017.
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  • A poem to illustrate the dependence between trials when sampling is without replacement. To set this poem up in the classroom, you might ask the students questions like: "If I want to put the Supreme Court Justices in a random order, I can pick one at a time without replacement. Before I pick the first Justice, do I know who it's going to be? Before I pick the last Justice, do I know who it's going to be?" The poem was written in 2017 by Larry Lesser from 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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