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Section 5.11 Key Takeaways
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ANOVA (Analysis of Variance) is used to compare
means across more than two groups.
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A
significant F-statistic indicates that at least one group mean is different.
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Post-hoc tests (like Tukey’s HSD) reveal
which groups differ from each other.
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The
Sum of Squares (SS) separates total variation into between-group and within-group sources.
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The
Mean Square (MS) is the average variation per degree of freedom (SS ÷ df).
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The
F value is a ratio comparing between-group to within-group variability.
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Two-way ANOVA adds an additional factor (e.g., caffeine level) to test main effects and interactions.
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AIC (Akaike Information Criterion) helps compare models —
lower values indicate better fit.
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Always visualize your data before and after running ANOVA to confirm patterns in group means.
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Reproducibility matters — use
set.seed() whenever you simulate or randomize data.
