The F-Test and F-Distribution › What's Next
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This chapter introduced the F-distribution and F-tests for comparing variances and testing regression models. The next chapter on ANOVA (Analysis of Variance) shows how the F-test extends to comparing means across three or more groups. ANOVA uses the same variance decomposition logic: partition total variation into between-group and within-group components, then test whether between-group variation is large enough to conclude the means differ.
After ANOVA, subsequent chapters cover:
- Type I and Type II errors: Understanding the two ways hypothesis tests can go wrong
- Power and sample size: Planning studies to detect meaningful effects
- Effect sizes: Quantifying practical significance beyond statistical significance
- Multiple comparisons: Controlling error rates when conducting many tests
All of these build on the foundation of understanding how variation is partitioned and compared, which you've learned through z-tests, t-tests, and now F-tests.