Significance Testing Principles

  • A song that may be used in discussing the definition and interpretation of the P-Value in significance testing. 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 Van Morrison’s 1967 classic rock song BlBrown Eyed Girl. Also, an accompanying video may be found at https://www.youtube.com/watch?v=TmQvXhN7Exc statistical topic: Significance Testing Principles – P-value

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  • A song to be used in discussing the idea that the null hypothesis represents the model of no effect (with several common examples). The original music and lyrics were written in 2017 by Greg Crowther from Everett Community College. The song won an honorable mention in the 2017 A-mu-sing contest. In the current 2018 version the music is by Greg Crowther and the revised lyrics and vocals are by Greg Crowther and Larry Lesser from University of Texas at El Paso.

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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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