module 4 Flashcards
(15 cards)
What is statistical power?
A: The probability that a study will detect an effect when one truly exists (Power = 1 - β).
What is a Type I error?
A: Incorrectly rejecting the null hypothesis when it is actually true (false positive).
What is a Type II error?
A: Failing to reject the null hypothesis when it is actually false (false negative).
What is the relationship between α (alpha) and Type I error?
A: Alpha is the probability of making a Type I error.
Why not always use a tiny alpha value?
A: A smaller alpha increases Type II errors (false negatives) and reduces power.
What factors influence statistical power?
A: Alpha level, sample size, effect size, and error variance.
How does increasing sample size affect power?
A: It increases power by reducing standard error and making the sampling distribution narrower.
What is effect size?
A: A standardized measure of the magnitude of an effect, expressed in standard deviation units.
What are common effect size measures for ANOVA?
A: Eta Squared (η²) and Omega Squared (ω²).
What are the benchmarks for Eta Squared?
A: Small = .01, Medium = .09, Large = .25.
What are the benchmarks for Omega Squared?
A: Small = .01, Medium = .06, Large = .14.
What effect size is used for planned contrasts?
A: r — Small = .10, Medium = .30, Large = .50.
What effect size is used for post-hoc tests like Tukey’s HSD?
A: Cohen’s d — Small = .20, Medium = .50, Large = .80.
What does G*Power software help with?
A: It calculates required sample size based on desired power and effect size.
Why is statistical power important before running an experiment?
A: To ensure the study can detect an effect if one exists, avoiding wasted effort.
