# Six Sigma Black Belt: Basics of Hypothesis Testing and Tests for Means

Everyone
• 8 videos | 1h 26m 38s
Rating 4.6 of 57 users (57)
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.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
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
Recognize how confidence intervals are used in statistical analysis
• distinguish between statements expressing confidence, tolerance, and prediction intervals
match definitions to key attributes of point estimates
Calculate the confidence interval for the mean and interpret the results in a given scenario, and calculate the tolerance interval in a given scenario
Perform key steps in a one-sample hypothesis test for means, and interpret the results
Test a hypothesis using a two-sample test for means

## IN THIS COURSE

• After completing this video, you will be able to discover the key concepts that will be covered in this course.
• After completing this video, you will be able to use key hypothesis testing concepts to interpret a testing scenario
• 3.  Practical and Statistical Significance
After completing this video, you will be able to recognize the implications of a hypothesis test result for statistical and practical significance
• 4.  Hypothesis Test Sample Size Considerations
After completing this video, you will be able to use the margin of error formula to determine sample size for a given alpha risk level
• 5.  Point Estimates and Interval Estimates
After completing this video, you will be able to match definitions to key attributes of point estimates, recognize how confidence intervals are used in statistical analysis, and distinguish between statements expressing confidence, tolerance, and prediction intervals
• 6.  Interval Estimate Calculation
After completing this video, you will be able to calculate the confidence interval for the mean and interpret the results in a given scenario and calculate the tolerance interval in a given scenario
• 7.  One-sample Hypothesis Tests for Means
After completing this video, you will be able to perform key steps in a one-sample hypothesis test for means, and interpret the results
• 8.  Two-sample Hypothesis Tests for Means
After completing this video, you will be able to test a hypothesis using a two-sample test for means

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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