Two errors in hypothesis testing:  Type I and Type II error

10-1: Define and describe Type I and Type II error.

10-2: Describe the probability of making (and not making) a Type I and Type II error.

10-3: Understand why it is difficult to calculate the probability of Type II error.

10-4: Understand why Type I and Type II error are a concern to researchers.

10-5: Understand why researchers allow for the possibility of making Type I and Type II errors.

10-6: Describe the concept of statistical power and its relationship to Type II error.

Controlling Type I and Type II error

10-7: Understand methods used to control Type I and Type II error as well as concerns with these methods.

10-8: Understand the relationship between the probability of making Type I and Type II errors.

10-9: Understand the difference between between-group variability and within-group variability.

Measures of effect size

10-10: Understand why it is inappropriate to use terms such as "highly significant" to describe analyses significant at the p < .01 or .001 level.

10-11: Understand why it is useful to include a measure of effect size when reporting the results of a statistical analysis such as the t-test.

10-12: Understand what it means for one variable to account for the variance of another variable.

10-13: Describe Cohen's (1988) guidelines for interpreting different effect sizes.

10-14: Understand why measures of effect size are comparable across different research studies.

10-15: Understand the difference between statistical significance and practical significance.

10-16: Understand the difference between the r2 statistic and Cohen’s d measure of effect size.