CompTIA Data+ (DA0-002)Data AnalysisMedium
A data analyst is investigating customer satisfaction scores (on a scale of 1-5) across different product lines. They want to know if there is a statistically significant difference in the *distribution* of satisfaction scores among Product A, Product B, and Product C. Which statistical test is most suitable for this analysis?
- APearson Correlation
- BKruskal-Wallis H-test
- COne-Way ANOVA
- DIndependent Samples t-test
Show answer & explanationAnswer & explanation
Correct answer: B. Kruskal-Wallis H-test
The Kruskal-Wallis H-test is a non-parametric test used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable. It is suitable when the assumption of normality for ANOVA is violated, or for ordinal data like satisfaction scores.
Why the other options are wrong
- A. Pearson Correlation measures the linear relationship between two continuous variables.
- C. One-Way ANOVA assumes normally distributed data and equal variances, and is typically for interval/ratio data, not strictly for distributions of ordinal scores.
- D. Independent Samples t-test compares means of two independent groups, not the distribution across three or more.
Kruskal-Wallis H-test
A non-parametric test used to compare the distributions of a continuous or ordinal variable for two or more independent groups. It is an alternative to One-Way ANOVA when assumptions are violated.
- Non-parametric (does not assume normality).
- Compares medians or ranks, not means.
- Suitable for ordinal data or non-normal interval/ratio data.
- Extension of the Mann-Whitney U test for more than two groups.
Memory trick: Kruskal 'K'eeps it non-parametric.