OUTLINE FOR CHAPTER
Effect Size, Power, CIs, and
- The Seven-Step Version of Hypothesis Testing
- A brief review of the simplest version of hypothesis testing
- The 7th step: After rejecting Ho,
determining the degree to which Ho
- The difference between "statistical significance" and "practical
- 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
- Hypothesis Testing Using Confidence Intervals
- Using a confidence interval to test a null hypothesis about a
- Using a confidence interval to test a null hypothesis involving
- 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