ABSTRACT
Effect size is a measure of the degree to which an independent variable is seen to affect a dependent variable or the degree to which two or more variables are related. As it is independent of the sample size it is useful for comparisons between studies.
The more powerful a statistical test, the more likely it is that a Type II error will be avoided. A major contributor to a test’s power is the sample size. During the design stage researchers should conduct some form of power analysis to decide on the optimum sample size for the study. If they fail to achieve statistical significance, then they should calculate what sample size would be required to achieve a reasonable level of statistical power for the effect size they have found in their study.
This chapter shows how to find statistical power using tables. However, computer programs exist for power analysis.
