A structured outline of the module's topics covering two-sample hypothesis tests, paired sample comparisons, and frameworks for selecting the appropriate statistical test, used by instructors to organize and sequence the module's content.
A navigation page linking learners to the Module 9 PowerPoint slides, lecture video, and Google Colab notebook for hypothesis testing with single samples.
A comprehensive reading covering single-sample hypothesis testing concepts including null and alternative hypotheses, test selection, and related procedures, used by learners as the primary reference text for the module.
This reading introduces two-sample hypothesis tests, explaining how to compare means or other statistics from two independent populations to determine whether observed differences are statistically significant, and serves as a foundational reference for learners setting up and interpreting such tests.
This reading explains paired sample designs, where each observation in one group is linked to a corresponding observation in the other, covering how the pairing structure changes the appropriate statistical method and how learners should apply paired tests rather than independent-samples tests in these situations.
This reading provides a structured decision-making framework for choosing the correct statistical test based on study design, data type, sample size, and other conditions, helping learners and instructors systematically avoid common errors in test selection.
This reading details the underlying assumptions required for inferential tests to produce valid probability statements—such as normality, independence, and equal variances—and guides learners on how to check these conditions before drawing conclusions from their analyses.
This reading introduces non-parametric statistical methods as alternatives to parametric tests when standard assumptions like normality cannot be met, explaining the rationale for and basic application of these techniques for learners working with real-world data that violates parametric requirements.
This reading explains how to correctly interpret and communicate the outputs of hypothesis tests—including test statistics, p-values, degrees of freedom, and confidence intervals—helping learners avoid common misinterpretations and report results accurately in engineering contexts.
A 45-question quiz bank featuring engineering-contextualized problems on selecting and applying hypothesis tests (t-tests, z-tests, proportion tests) for single and two-sample scenarios, used by instructors to assess learners' ability to choose and execute the correct statistical test.
A graded discussion assignment requiring learners to query an AI about statistical methods relevant to Six Sigma Black Belt certification and then reflect on the feasibility and professional implications of pursuing that credential.
A graded assignment in which learners analyze real tensile strength data for two aluminum alloys using two-sample hypothesis testing to determine whether Alloy B performs significantly better than Alloy A.