Lean Six Sigma Green Belt Practice Exam — All Questions

When testing whether a process change produced a statistically significant effect, a p-value of 0.02 against an alpha of 0.05 means:

• a.Reject the null hypothesis; the result is statistically significant✓
• b.Fail to reject the null hypothesis; no significant effect
• c.The test is invalid and must be repeated
• d.The effect size is guaranteed to be large

Because the p-value (0.02) is less than alpha (0.05), you reject the null hypothesis and conclude the effect is statistically significant. Statistical significance does not by itself measure the size or practical importance of the effect.

A Pareto chart supports the Analyze phase by helping the team:

• a.Prove causation between two variables
• b.Monitor a process over time for stability
• c.Focus on the 'vital few' categories that account for most of the problem✓
• d.Estimate the population standard deviation

The Pareto principle directs attention to the small number of categories that produce the majority of the defects or cost. It does not establish causation, track stability over time, or estimate spread.

A correlation coefficient (r) of 0.85 between two variables indicates:

• a.That one variable definitely causes the other
• b.A strong positive linear relationship between the variables✓
• c.No relationship between the variables
• d.A strong negative linear relationship

An r near +0.85 signals a strong positive linear association, meaning the variables tend to rise together. Correlation alone never proves causation, and a negative relationship would show a negative r.

The '5 Whys' technique is primarily used to:

• a.Calculate process capability indices
• b.Set the sample size for a study
• c.Build a control chart
• d.Drill down from a symptom to an underlying root cause✓

Asking 'why' repeatedly moves the team from the visible symptom toward the deeper root cause. It is a qualitative root-cause tool, not a method for capability, sampling, or control charting.

In hypothesis testing, a Type I error (alpha) occurs when you:

• a.Reject a null hypothesis that is actually true (a false positive)✓
• b.Fail to reject a null hypothesis that is actually false
• c.Choose too large a sample size
• d.Measure a part with the wrong gage

A Type I error is a false positive: concluding an effect exists when it does not, which happens with probability alpha. Failing to detect a real effect is a Type II (beta) error.