Power and Sample Size

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Now that we've covered the null hypothesis significance testing (NHST) framework and how we use it for statistical inference, we can move to one of the most feared and misunderstood topics in BIOS 6611...power and sample size calculations! • • • • In this lecture we will derive closed form solutions for power calculations for a two-sided, one-sample Z-test. There are 5 important quantities in almost any power/sample size calculation that are discussed: level of significance, power, sample size, effect size, and variability. Examples for our two-sided, one-sample Z-test are provided. We end with a discussion of what power calculations do and don't provide in practice. • • • • Note, the next lecture introduces more examples using R for a variety of scenarios. • • • • A video for the Biostatistical Methods I (BIOS 6611) course in the Department of Biostatistics and Informatics at the University of Colorado-Anschutz Medical Campus taught by Dr. Alex Kaizer. Slides and additional material available at https://www.alexkaizer.com/bios_6611. • • Table of Contents: • • 00:00 - Intro Song • 00:15 - Welcome • 00:59 - Important Definitions for Power Calcs • 05:11 - Power Calc: 2-Sided 1-Sample Z-test • 06:23 - The 5 Important Power Calc Quantities • 14:39 - 2-Sided, 1 -Sample Z-test Formulas • 16:35 - Sample Size Example • 19:17 - Power Example • 21:23 - Detectable Difference Example • 22:32 - One-Sided Power Calculations • 23:21 - What a Power Calc Does (and Doesn't) Do

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