P-values and Hypothesis Test Setup › What's Next
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In the next chapter, Confidence Intervals and Test Assumptions, we'll explore:
- The deep connection between confidence intervals and hypothesis tests (they're mathematically equivalent!)
- The assumptions underlying common tests and what happens when they fail
- How to choose between z-tests and t-tests
- Welch's t-test as a reliable default for comparing means
After that, you'll examine specific tests: the z-test for when population variance is known, the t-test for when it's unknown, F-tests for comparing variances, and ANOVA for comparing multiple groups. Later chapters will cover Type I and Type II errors, power analysis, effect sizes, and multiple comparison corrections.
Each chapter builds on what you've learned here. The logic of hypothesis testing (assume nothing, measure surprise, make decisions) remains the same throughout. Only the specific formulas change.