1. ANalysis Of VAriance (ANOVA) Used for continuous outcomes with a nominal exposure with three or more categories (groups) Result of test is F statistic Interpretation of p-value is same as that of chi square and t-test

2. A nota about ANOVA If p < α, which group comparison(s) account(s) for the significant finding? Big issue: Multiple comparisons More groups, greater number of comparisons Better chance to commit a Type I error (alpha)

3. Multiple comparisons in ANOVA Many methods to correct for this Most common: Bonferroni adjustment Adjusts alpha based on number of comparisons For four groups (A, B, C, D), there are six comparisons (AB, AC, AD, BC, BD, CD) Alpha is set to 0.05/6 = 0.0083 Can be overly conservative if there are many groups Others include Tukey, Scheffe, LSD LSD: least significant difference

4. Come to terms with these… Type I error (α) occurs when the null hypothesis is rejected when it shouldn’t be Type II error (β) occurs when the null hypothesis is not rejected when it should be Power (1 – β) is the ability to reject the null hypothesis when it should be rejected Chi-square tests are used to test associations between nominal (often binary) variables Two-sample t-tests are used to test for differences in means between two groups p-value is the probability of obtaining a test statistic or result at least as extreme as the one you obtained by chance alone, if the null hypothesis is true