In this video, Dr. Richard Chua explains how to determine sample sizes to ensure sufficient power in hypothesis tests for Six Sigma projects.
- When you run a hypothesis test,…there's always a chance of committing a type two error.…Which is not rejecting the null hypothesis…when it is, in fact, false.…The probability of that happening is called beta.…You want beta to be as small as possible.…The hypothesis test should have sufficient power…to be able to reject…the null hypothesis when it is false.…The power of a test is the probability…of correctly rejecting the null hypothesis,…when it's, in fact, false.…
The higher the power, the smaller the beta.…To be exact, power equals one minus beta.…So, what effects power?…It depends on the test.…But power is largely dependent on sample size,…the variation in the data,…and since power is the ability…to detect a difference when there is one,…the smaller the difference to be detected,…the higher the power that's needed.…For example, to be able to detect…and prove a difference of two minutes,…between the mean processing times of two groups,…requires a more powerful test…than one which needs to detect…a difference of 10 minutes.…
Dr. Richard Chua builds upon his Six Sigma: Green Belt, Six Sigma Foundations, and Learning Minitab courses—which are prerequisites to this course—and covers an array of topics, including measurement system analysis, hypothesis testing, response surface methods, displaying improved process capabilities, and more.
- Process flow metrics
- Measurement system analysis
- Calculating process capability
- Hypothesis testing
- Confidence intervals
- Testing for normality
- Designing, conducting, and analyzing full-factorial experiments
- Using fractional factorial experiments for screening
- Displaying improved process capability
Skill Level Intermediate
1. Define Phase
Overview of the Define phase3m 51s
2. Measure Phase
3. Analyze Phase
4. Improve Phase
5. Control Phase
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