ANOVA (Analysis of Variance) › What's Next
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This chapter covered one-way ANOVA for comparing means across multiple groups. The following chapters extend your understanding of hypothesis testing:
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Type I and Type II Errors explores the two ways hypothesis tests can fail: false positives and false negatives. Understanding these errors is essential for interpreting ANOVA results and designing studies.
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Power and Sample Size shows how to plan studies with adequate power to detect meaningful effects, connecting directly to the effect sizes you learned to calculate here.
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Effect Sizes provides deeper coverage of measuring practical significance, including alternatives to eta-squared and guidelines for interpretation.
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Multiple Comparisons revisits the family-wise error rate problem in depth, covering false discovery rate control alongside correction methods such as Bonferroni and Holm.
The ANOVA framework you've learned here extends naturally to more complex designs: two-way ANOVA (two grouping factors), factorial ANOVA (multiple factors with interactions), and repeated measures ANOVA (same subjects measured multiple times). These advanced topics build directly on the variance decomposition logic you now understand.