PSYC*2120 Study Guide - Final Guide: Type I And Type Ii Errors, Null Hypothesis, Sample Size Determination
Document Summary
Factors that affect a hypothesis test: the size of the mean difference (m ), the variability of the scores, the sample size, significance level alpha. Assumptions for hypothesis tests with z-scores: random sampling, independent observations, is unchanged by the treatment, normal sampling distribution. A type i error occurs when a researcher rejects a null hypothesis that is actually true. This means that the researcher concludes that a treatment has an effect when in fact it does not have an effect. A type ii error occurs when the researcher fails to reject a false null hypothesis. That is, a treatment effect really does exist, but the data was such that the hypothesis failed to detect it. Power is the probability that the hypothesis will correctly lead to the rejection of a false null hypothesis. It is the probability that if a treatment effect really does exist, the test will identify it. p(type i) = p(type ii) = .
