Week 11 / Module 10 — 2 Sample Hypothesis Testing

A structured outline of the module’s learning topics, covering two-sample hypothesis testing foundations, independent versus paired samples, assumptions, and related statistical conditions, used as a navigational reference for what the module addresses.

A landing page that organizes and links to the module’s core instructional resources — the PowerPoint slides, lecture video, and Google Colab notebook — for learners to access all materials in one place.

A detailed study reference explaining key two-sample hypothesis testing methods, including the independent samples t-test, paired t-test, and tests for two proportions, with definitions, characteristics, and supporting lecture links for learners to review concepts.

This reading introduces the conceptual framework of two-sample hypothesis testing, explaining why comparative questions arise in engineering and science and how two-sample tests extend single-sample inference; learners use it to build foundational understanding before selecting and applying specific tests.

This reading explains the critical distinction between independent and paired samples, covering how each design is structured, how pairing affects variability and test selection, and when to use each approach; learners and instructors use it to ensure the correct test type is chosen before any analysis begins.

This reading outlines the statistical assumptions underlying pooled two-sample t-tests, Welch’s t-test, and non-parametric alternatives such as the Mann-Whitney U test, explaining how to check each condition and what violations mean for test validity; learners use it as a pre-analysis checklist before running two-sample tests.

This reading covers the procedures for comparing two population means using pooled and Welch’s t-tests, including hypothesis formulation, test-statistic calculation, p-value interpretation, and confidence intervals for the difference in means; learners use it as a step-by-step procedural guide for mean-comparison problems.

This reading explains the two-proportion z-test, covering how to set up hypotheses, calculate the pooled proportion and test statistic, interpret p-values, and construct confidence intervals for the difference between two population proportions; learners use it to analyze comparative rate or proportion problems.

This reading focuses on translating hypothesis test outputs—p-values, test statistics, and confidence intervals—into clear, context-specific conclusions and data-driven decisions; learners use it to practice communicating statistical results meaningfully rather than stopping at numerical output.

A 30-question quiz bank assessing learners’ ability to select appropriate two-sample statistical tests and apply hypothesis testing procedures across engineering and applied scenarios, used by instructors to build graded quizzes.

A graded discussion assignment asking learners to describe Design of Experiments within Six Sigma methodology and apply it to a real-world personal or professional scenario, prompting reflection and peer engagement.

A graded assignment presenting a paired before-and-after engineering scenario involving a new engine coolant additive, in which learners must conduct a two-sample hypothesis test to determine whether the additive significantly reduces engine operating temperature.