Hypothesis Testing Applications Flashcards
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A Six Sigma team wants to determine if there is a statistically significant difference in the average processing time (a continuous variable) among three different operational shifts. Which of the following hypothesis tests is the most appropriate to use?
Answer: ANOVA (Analysis of Variance)
ANOVA is used to compare the means of three or more groups. A 2-sample t-test is only suitable for comparing the means of two groups. A paired t-test is used when the data points are related (e.g., before and after measurements on the same subject). The Chi-Square test is used for analyzing categorical data, not continuous data like processing time.
A project team implemented a process change to reduce the proportion of defective products. A 2-proportion test was conducted, yielding a p-value of 0.025. Using a standard significance level (alpha) of 0.05, what is the correct conclusion?
Answer: Reject the null hypothesis; the change resulted in a statistically significant reduction in defects.
The decision rule in hypothesis testing is to compare the p-value to the significance level (alpha). If the p-value is less than or equal to alpha, the null hypothesis is rejected. Here, 0.025 is less than 0.05, so we reject the null hypothesis, concluding there is a statistically significant difference.
A Black Belt is preparing to compare the mean performance of two independent groups using a 2-sample t-test. For the results of this parametric test to be valid, which of the following is a critical assumption?
Answer: The data from both populations should be approximately normally distributed.
The 2-sample t-test is a parametric test that assumes the underlying data from both populations are approximately normally distributed. While the test is robust to minor departures from normality, it is a core assumption. The test does not require equal sample sizes, and there are versions of the t-test (like Welch's t-test) that do not assume equal variances. The data must be continuous, not attribute data.
A quality manager wants to determine if there is a statistically significant association between the type of paint defect (e.g., 'Cracking', 'Peeling', 'Blistering') and the production line ('Line A', 'Line B', 'Line C') on which the product was painted. What is the most appropriate statistical test for this analysis?
Answer: Chi-Square Test of Independence
The Chi-Square Test of Independence is used to determine if there is a significant association between two categorical variables. In this scenario, 'Defect Type' and 'Production Line' are both categorical variables. ANOVA is for comparing means of continuous data across groups, a 1-proportion test is for a single categorical variable against a standard, and Pearson Correlation measures the linear relationship between two continuous variables.
A team is testing a new manufacturing process to see if it reduces the average cycle time, which has historically been 45 minutes. Which of the following represents the correct null hypothesis (H0) for a one-tailed test to determine if the new process is an improvement?
Answer: H0: μ ≥ 45 minutes
The null hypothesis (H0) represents the status quo or the condition of 'no effect'. Since the team is looking for an improvement (a reduction), the alternative hypothesis (Ha) would be that the mean is less than 45 minutes (Ha: μ < 45). The null hypothesis must therefore cover all other possibilities, which is that the mean is greater than or equal to the historical average (H0: μ ≥ 45 minutes).
Before conducting a 2-sample t-test, a Black Belt wants to check the assumption of equal variances between the two independent samples. Which statistical test is specifically designed to compare the variances of two populations?
Answer: F-test
The F-test is used to test the hypothesis that the variances of two populations are equal. This is often done as a preliminary step before a 2-sample t-test to decide whether to assume equal variances. A t-test compares means, ANOVA compares means of more than two groups, and a Goodness-of-Fit test compares observed frequencies to expected frequencies for one categorical variable.