Analyse: Data & Hypothesis Testing
49 practice questions with explanations — 15 free to try
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PassNova has 49 Lean Six Sigma Green Belt practice questions on Analyse: Data & Hypothesis Testing, each with a clear explanation. A 15-question taster is free with no sign-up; the full bank is part of PassNova Premium. Updated for 2026.
Analyse: Data & Hypothesis Testing: example questions & answers
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In hypothesis testing, what is the correct decision rule when the calculated p-value is less than the chosen significance level alpha?
- AAccept the null hypothesis as proven true
- BReject the null hypothesis✓
- CIncrease the sample size and retest
- DFail to reject the null hypothesis
Answer: When p is less than alpha, the result is statistically significant and the null hypothesis is rejected in favour of the alternative.
A Type I error in hypothesis testing is best described as:
- ARejecting a null hypothesis that is actually true✓
- BChoosing the wrong statistical test for the data
- CUsing a sample that is too small to detect an effect
- DFailing to reject a null hypothesis that is actually false
Answer: A Type I error (false positive) occurs when a true null hypothesis is incorrectly rejected; its probability equals the significance level alpha.
A Type II error occurs when an analyst:
- AFails to reject a false null hypothesis✓
- BSets the significance level too low before testing
- CConfuses correlation with causation in the results
- DRejects a true null hypothesis on the basis of a small sample
Answer: A Type II error (false negative) is failing to reject a null hypothesis that is actually false; its probability is denoted beta and 1 minus beta is the test's power.