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14b_Ch13_One-Factor_ANOVA_Independent_Outline

# 14b_Ch13_One-Factor_ANOVA_Independent_Outline - 1 2...

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One-Factor Analysis of Variance Assumptions, ANOVA Table, Effect Size, Post Hoc Tests, t and F for Two Independent Samples Assumptions ANOVA (Independent Samples) 1. Independent observations within each sample 2. The populations from which the sample were drawn must be normal 3. Homogeneity of Variance — the populations from which the samples are drawn must have equal variances ANOVA Summary Table Effect Size Eta Squared Post Hoc Tests Tukey’s HSD Scheffé Post Hoc Tests • If we reject H 0 , we know that not all of the means are the same Post Hoc Tests • But we don’t know which means are different Post Hoc Tests • When you have more than two population means, a test of multiple comparisons enables you to test a series of differences Post Hoc Tests • For example, a test of multiple comparisons would allow us to test the following differences: Why not just use a t test? t tests are designed for single comparisons • Multiple t tests increase the probability of a Type I error

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