OUTLINE FOR CHAPTER 8 Effect Size, Power, CIs, and Bonferroni The Seven-Step Version of Hypothesis Testing Introduction: A brief review of the simplest version of hypothesis testing The 7th step: After rejecting Ho, determining the degree to which Ho was wrong The difference between "statistical significance" and "practical significance" Two ways researchers can do Step #7 They can compute a measure of "effect size" They can conduct a post hoc "power analysis" The Nine-Step Version of Hypothesis Testing A simple listing of the nine steps . . . with the 3 "new" steps located in positions 4, 5, and 6 Step #4: Specification of the effect size (ES): ES: the point that separates cases where Ho is false by a small and trivial amount vs. cases where it's false by a big and noteworthy amount Two options for ES: "Raw" or "standardized" (and Cohen's "standards") Step #5: Specification of the desired level of "power": The notions of "statistical power" and a "beta error" Why researchers donšt set power at .999 Step #6: Determination of the needed sample size: Computing n from a formula or looking up the needed n in a chart What to do if there is a fixed n The primary advantage of using the 9-step version of hypothesis testing Hypothesis Testing Using Confidence Intervals Using a confidence interval to test a null hypothesis about a single population Using a confidence interval to test a null hypothesis involving two populations Adjusting for an Inflated Type I Error Rate The notion of an "inflated Type I error rate" Why the Type I error rate becomes "inflated" if a fixed alpha is used with multiple tests The Bonferroni adjustment technique The experimentwise error rate The Dunn-Sidak modification A Few Cautions Two meaning of the term "effect size" "Small," "medium," and "large" effect sizes The simplistic nature of the 6-step version of hypothesis testing Inflated Type I error rates