Hypothesis Testing Concepts and Tests for Means in Six Sigma

In the Analyze phase of the DMAIC methodology, Six Sigma teams analyze the underlying causes of issues that need to be addressed for the successful completion of their improvement projects. To that end, teams conduct a number of statistical analyses to determine the nature of variables and their interrelationships in the process under study. It is rarely possible to study and analyze the full scope of population data pertaining to all processes, products, or services, so Six Sigma teams typically collect samples of the population data to be analyzed, and based on that sample data, they make hypotheses about the entire population.

Because there is a lot at stake in forming the correct conclusions about the larger population, Six Sigma teams validate their inferences using hypothesis tests. This course builds on basic hypothesis testing concepts, terminologies, and some of the most commonly used hypothesis tests one- and two-sample tests for means. The course also discusses the importance of sample size and power in hypothesis testing, as well as exploring issues relating to point estimators and confidence intervals in hypothesis testing. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in SkillSoft's ASQ-aligned Green Belt curriculum.

• use key hypothesis testing concepts to interpret a testing scenario
• recognize the implications of a hypothesis test result for statistical and practical significance
• use the margin of error formula to determine sample size for a given alpha risk level
• match definitions to key attributes of point estimates
• distinguish between statements expressing confidence, tolerance, and prediction intervals
• recognize how confidence intervals are used in statistical analysis
• calculate the confidence interval for the mean and interpret the results in a given scenario
• calculate the tolerance interval in a given scenario
• perform key steps in a one-sample hypothesis test for means, and interpret the result
• test a hypothesis using a two-sample test for means