Basic Statistical Tools Flashcards
7 cards from real Lean Six Sigma Green Belt Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Basic Statistical Tools flashcards as text
Which central limit theorem property allows the use of normal-distribution-based control charts even when the underlying data are non-normal?
Answer: Sample means approach a normal distribution as sample size increases
The Central Limit Theorem states that the distribution of sample means becomes approximately normal as n increases, regardless of the population distribution.
A Gauge R&R study reveals that measurement system variation accounts for 28% of total observed variation. What action is most appropriate?
Answer: Improve or replace the measurement system before collecting process data
A %GR&R above 30% is unacceptable; 28% is in the marginal zone and improvement is strongly recommended before analysis.
What does the standard error of the mean (SEM) measure?
Answer: How much sample means vary from the population mean across repeated samples
SEM = σ / √n, representing how much the sample mean is expected to vary from the true population mean.
A process has Cp = 1.50 and Cpk = 0.80. What does this tell you?
Answer: The process has adequate spread but is significantly off-center
High Cp with low Cpk indicates the process spread fits within specs but the mean is shifted toward or beyond one limit.
When should a nonparametric statistical test be used instead of a parametric test?
Answer: When the data are ordinal or clearly non-normal with small sample sizes
Nonparametric tests make no normality assumption and are appropriate for ordinal data or non-normal distributions, especially with small samples.
In the context of control charts, what is the purpose of control limits set at ±3σ?
Answer: To identify when process variation is likely due to special causes
±3σ control limits represent the range of expected common-cause variation; points beyond them signal likely special causes.
A team finds that their process data exhibit positive skewness. Which statement best describes this distribution?
Answer: The right tail is longer and the majority of data falls on the left
Positive (right) skewness means the distribution has a longer right tail, with most data concentrated on the left side.