Overview

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BiteSize Statistics for Business: Hypothesis Testing for Single Samples is the first course in the BiteSize Stats for Business specialization. It moves from probability theory to inferential statistics: given one sample, how do you rigorously test a claim about the population it came from — and how confident should you be in the answer? Across five modules, learners progress from the logic of hypothesis testing (hypotheses, error types, p-values, decision rules), through one-sample Z-tests and t-tests for means and proportions with their matching confidence intervals, to the chi-square goodness-of-fit test for categorical data, and finally statistical power, effect size, and sample size planning. Every core lesson pairs a focused reading with a hands-on interactive notebook built around a realistic business scenario, and every module includes a fully worked demo case study and an ungraded practice project applying the module's tools to a new scenario.

Syllabus

The Logic of Hypothesis Testing
- Introduces hypothesis testing as a structured procedure for evaluating a claim using sample evidence, framed through the court-of-law analogy and the five-step testing procedure. Students write null and alternative hypotheses correctly, distinguish Type I from Type II errors, define and interpret the p-value, and apply the decision rule while distinguishing statistical from practical significance.
One-Sample Z-Tests and Confidence Intervals
- 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.
One-Sample t-Tests and Confidence Intervals
- 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.
The Chi-Square Goodness-of-Fit Test
- 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.
Power, Effect Size, and Sample Size
- 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.