Advanced Hypothesis Testing Flashcards
7 cards from real Certified Six Sigma Black Belt Exam practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Advanced Hypothesis Testing flashcards as text
A Six Sigma team wants to compare the variances of two independent populations. Which test is most appropriate?
Answer: F-test (Snedecor's F)
The F-test (Snedecor's F) is the classical test for comparing two population variances using the ratio of sample variances.
When the p-value equals 0.049 and alpha is 0.05, a Black Belt should:
Answer: Reject H₀ and consider practical significance before acting
A p-value below alpha leads to rejecting H₀, but statistical significance must always be weighed against practical/economic significance.
Which assumption is NOT required for a standard two-sample t-test?
Answer: The sample means follow a chi-square distribution
Sample means follow a t-distribution (not chi-square) under normality assumptions; chi-square applies to variance tests.
A Kruskal-Wallis test is the nonparametric alternative to which parametric test?
Answer: One-way ANOVA
The Kruskal-Wallis test compares medians across three or more independent groups, serving as the nonparametric equivalent of one-way ANOVA.
In hypothesis testing, the power of a test is defined as:
Answer: 1 minus the probability of a Type II error
Power = 1 − β, where β is the probability of a Type II error (failing to reject a false H₀).
A Black Belt uses a one-tailed test instead of a two-tailed test. Compared to two-tailed at the same alpha, the one-tailed test:
Answer: Has greater power to detect effects in the hypothesized direction
By concentrating all of alpha in one tail, a one-tailed test has greater power to detect effects in the predicted direction.
Which scenario correctly describes a Type I error in a manufacturing hypothesis test?
Answer: Concluding a process is out of control when it actually is in control
A Type I error (false positive) occurs when H₀ is true but is incorrectly rejected — concluding a problem exists when there is none.