Biostatistics Final Exam Definitions UTA 100% Correct

Biostatistics Final Exam Definitions UTA 100% Correct Bonferroni-Holm Multiple Comparisons -In the context of an ANOVA, rejecting the null hypothesis means there is a statistical difference between at least two means, but it won't tell you where it is. You use this to discover exactly which means are different. -Only used for Model I ANOVAs -A t-statistic for a two sample t-test assuming equal variance Steps to Applying the Bonferroni-Holm Multiple Comparison Test Step 1: Calculate test statistics and corresponding P values for each of the m comparisons Step 2: Order the P values from smallest to largest: p1,p2,...,pm. Label the corresponding comparisons: C1,C2,...Cm Step 3: Compare p1 and alpha(a)/m (a) If p1 a/m, then stop. Conclude that there is no evidence of differences between any of the means. Procedure is done (b) p1 or = a/m, then reject Ho for C1. Continue to next step. Step 4: Compare p2 and a/(m-1) (a) If p2 a/(m-1), then stop. There is no evidence of differences between any of the means in the remaining comparisons. Procedure is done. (b) p2 or = a/(m-1), then reject Ho for C2. Continue to next step. Step 5: Compare p3 and a/(m-2) (a) If p3 a/(m-2), then stop. There is no evidence of differences between any of the means in the remaining comparisons. Procedure is done. (b) p3 or = a/(m-2), then reject Ho for C3. Continue to next step Continue until procedure requires you to stop or until all P values have been compared Kruskal-Wallis Test The nonparametric analog to a Model I One-Way ANOVA. We would use if it we reject the assumption of normality for the data. Randomized-Complete Block Design ANOVA Used to extend paired experimental designs to accommodate making more than just two measurements on the same individuals. Also called Repeated Measures ANOVA. Model Assumptions: 1. Each observation constitutes a random, independent sample from a population with mean u_ij. There are k x b of these populations sampled. 2. Each of the k x b populations is normal and with the same variance. 3. The treatment and block effects are additive, that is, there is no interaction (synergy or interference) between blocks and treatments. Factorial-Design Two-Way ANOVA Model