Covers the one-sample Z-test for a mean and for a proportion, including the required conditions and the five-step testing procedure with a known population standard deviation. Students then construct confidence intervals and learn the duality between a two-tailed Z-test and a confidence interval, applying both to a website click-through-rate case study and a self-selected business claim.

Introduces the Student t-distribution and why it replaces the Z-distribution when the population standard deviation is unknown, then the one-sample t-test and t-based confidence intervals. Students apply the CI-test duality and compare t- and Z-based intervals, closing with delivery-time benchmarking case studies at two sample sizes.

Introduces the chi-square distribution and its relationship to the standard normal, then the goodness-of-fit test for comparing observed frequencies to a claimed distribution. Students calculate expected frequencies and the chi-square statistic, apply the full five-step testing procedure, and use per-cell contributions to identify which categories deviate most from expectations.

Defines statistical power and the factors that determine it, then derives sample size formulas for means and proportions given a target margin of error or power. Students learn to interpret a non-significant result in light of power and apply sample size planning before data collection, closing with survey-design and power-comparison case studies.