Medium Effect Size (applying d = .5): Cohen’s tests - Means: Difference between two independent means (two groups) t A priori: Compute required sample size Analysis: Tail(s) = Two Input: Effect size = 0.5 d err prob = 0.05 α Power (1-β err prob) = 0.80 Allocation ratio N2/N1 = 1 Output: Critical = 1.978971 t = 126 df Sample size group 1 = 64 Sample size group 2 = 64 Total sample size = 128 Actual power = 0.801460 Very Large Effect Size (applying d = 1.5): Cohen’s tests - Means: Difference between two independent means (two groups) t Analysis: A priori: Compute required sample size Input: Tail(s) = Two Effect size = 1.5 d err prob = 0.05 α Power (1-β err prob) = 0.80 Allocation ratio N2/N1 = 1 Output: Critical = 2.119905 t
Medium Effect Size (applying Cohen’s d = .5): t tests - Means: Difference between two independent means (two groups) Analysis: A priori: Compute required sample size Input: Tail(s) = Two Effect size d = 0.5 α err prob = 0.05 Power (1-β err prob) = 0.80 Allocation ratio N2/N1 = 1 Output: Critical t = 1.978971 df = 126 Sample size group 1 = 64 Sample size group 2 = 64 Total sample size = 128 Actual power = 0.801460 Very Large Effect Size (applying Cohen’s d = 1.5): t tests - Means: Difference between two independent means (two groups) Analysis: A priori: Compute required sample size Input: Tail(s) = Two Effect size d = 1.5 α err prob = 0.05 Power (1-β err prob) = 0.80 Allocation ratio N2/N1 = 1 Output: Critical t = 2.119905