The Z-Test › What's Next
Machine Learning from ScratchAll chapters The Z-Test › What's NextOne section · Sections in this chapter10 / 11 ↓
Reading tools Definitions & inline links
Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.
Article links
Make inline references clickable
In the next chapter, The T-Test, we tackle the far more common situation: testing hypotheses when the population standard deviation is unknown. The t-test uses the sample standard deviation instead, which introduces additional uncertainty accounted for by the t-distribution's heavier tails.
You'll learn about one-sample t-tests, independent two-sample t-tests (including Welch's version), and paired t-tests for dependent samples. The conceptual framework is identical to what you've learned here; only the distribution changes.
After t-tests, subsequent chapters cover F-tests for comparing variances, ANOVA for comparing multiple groups, and advanced topics including power analysis, effect sizes, and multiple comparison corrections.