Root cause analysis tools including fishbone diagrams, five whys, Pareto charts, and hypothesis testing used in the Analyze phase.
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- What does a fishbone diagram show?
- Potential causes of a problem organized by category branches extending from a central spine representing the effect
- What are the six Ms typically used as main categories in an extended fishbone diagram?
- Materials, Methods, Machines, Man (People), Measurement, and Mother Environment (Environment)
- What is another name for a fishbone diagram?
- Ishikawa diagram
- When should you use a fishbone diagram instead of the Five Whys?
- When you want to identify and organize many potential causes at the same level before drilling down, rather than sequentially asking why
- What is the Five Whys technique?
- A problem-solving method where you repeatedly ask 'why' to drill down through successive layers of cause and effect to find the root cause
- What does a Pareto chart display?
- A bar chart ranked by frequency or impact in descending order, with a cumulative percentage line overlay showing total impact
- State the Pareto principle and the typical ratio it describes.
- Approximately 80 percent of the effects (problems) come from 20 percent of the causes
- In a Pareto chart, what does the cumulative percentage line help you identify?
- The vital few causes that account for the majority of the problem, so you can prioritize which causes to address first
- What is the null hypothesis in statistical testing?
- The assumption that there is no significant difference, no effect, or no relationship between variables
- What is the alternative hypothesis?
- The hypothesis that there is a statistically significant difference, effect, or relationship; it is accepted if the null hypothesis is rejected
- What does a p-value represent?
- The probability of observing the test result (or more extreme) if the null hypothesis is actually true
- What is the standard alpha level (significance level) used to reject the null hypothesis?
- 0.05 (or 5 percent), meaning you accept a 5 percent chance of a Type I error
- What is a Type I error in hypothesis testing?
- Rejecting the null hypothesis when it is actually true; concluding there is a significant effect when there is not
- What is a Type II error in hypothesis testing?
- Failing to reject the null hypothesis when it is actually false; concluding there is no effect when there really is one
- What does the power of a test measure?
- The probability of correctly rejecting the null hypothesis when it is false; power equals 1 minus the Type II error rate
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