CompTIA Data+ (DA0-002)Data AnalysisMedium
A data analyst is performing a hypothesis test to compare the average customer satisfaction scores between three different product lines (Product A, Product B, and Product C). The scores are collected on a continuous scale, and the analyst assumes that the data is normally distributed and the variances across the groups are approximately equal. Which statistical test is most appropriate for this scenario?
- AANOVA (Analysis of Variance)
- BPaired t-test
- CIndependent samples t-test
- DChi-square test
Show answer & explanationAnswer & explanation
Correct answer: A. ANOVA (Analysis of Variance)
ANOVA (Analysis of Variance) is the appropriate statistical test when comparing the means of three or more independent groups to determine if at least one group mean is statistically different from the others, assuming normality and homogeneity of variances.
Why the other options are wrong
- B. Paired t-test is used for comparing means of two dependent (matched) samples.
- C. Independent samples t-test is used for comparing only two independent group means.
- D. The Chi-square test is used for categorical data, not for comparing means of continuous scores.
ANOVA (Analysis of Variance)
A statistical test used to compare the means of three or more independent groups to determine if there is a statistically significant difference between them.
- Assumes normality and homogeneity of variances (equal variances across groups).
- Uses the F-statistic to test the null hypothesis that all group means are equal.
- If significant, further post-hoc tests are needed to identify which specific groups differ.
Memory trick: ANOVA is like a 'committee meeting' for means, checking if all groups agree or if someone stands out.