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Notes on Topic 13:
One-Way Analysis of Variance

    Preview of ANOVA

    Definition of Analysis of Variance

    Analysis of Variance (ANOVA) is a hypothesis testing procedure that is used to evaluate differences between the means of two or more treatments or groups (populations). ANOVA uses sample data to make inferences about populations.

    Goals of ANOVA

    Conceptually, the goal of ANOVA is to determine the amount of variability in groups of data, and to see if the variability is greater between groups than within groups.

    ANOVA & T-Tests:

    ANOVA is a more general version of the t-test in two ways:
    1. Both tests use sample data to test hypotheses about population means. ANOVA, however, can test hypotheses about two or more population means. The T-Test can only test hypotheses about two population means.
    2. T-Test can only be used with one independent (classification) variable, whereas ANOVA can be used with more any number of independent (classification) variables.

    Like the T-Test, ANOVA can be used with either independent or dependent measures designs. That is, the several measures can come from several different samples (independent measures design), or they can come from repeated measures taken on the same sample of subjects (repeated --- dependent --- measures design).


    • Chapter 13 covers only the very simplest type of ANOVA: One-Way Independent Measures ANOVA (called "single-factor Independent Measures" designs in the book).
    • Chapter 14 covers one-way (single-factor) repeated measures designs. We don't cover this.
    • Chapter 15 covers two-way independent measures designs. I'll talk about this briefly.