DA0-002 Data Analysis Practice Question
A data analyst needs to determine whether the mean sales of two different regions are significantly different. The samples are independent and the data is normally distributed. Which statistical test should be used?
⚠ Common exam trap
CompTIA often tests the distinction between independent and paired t-tests, trapping candidates who overlook the 'independent samples' condition and mistakenly choose the paired t-test for any two-group comparison.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Independent samples t-test
The independent samples t-test is the correct choice because the scenario involves comparing the means of two independent groups (two different regions) with normally distributed data. This test specifically assesses whether the difference between the two sample means is statistically significant, assuming equal or unequal variances as determined by Levene's test.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Chi-square test for independence
Why it's wrong here
Chi-square tests association between two categorical variables via observed versus expected frequencies, so it cannot compare means of a continuous measure like sales. It is tempting because both involve two groups, but it would be the correct choice only for testing whether region and a categorical outcome such as purchase status are independent.
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ANOVA
Why it's wrong here
ANOVA compares means across three or more groups using between-group and within-group variance; with only two regions it is unnecessary and yields the same result as an independent t-test. It is tempting because it does test mean differences, and it would be correct if the analyst were comparing three or more regional means simultaneously.
- ✓
Independent samples t-test
Why this is correct
Two independent, normally distributed samples compared on a continuous mean call for the independent samples t-test, which assesses whether the difference between group means exceeds sampling variability. A paired test would require matched observations, which the stem excludes.
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Paired t-test
Why it's wrong here
A paired t-test analyses dependent observations, such as the same subjects measured twice, by testing the mean of the within-pair differences. The stem specifies independent samples from two regions, so no natural pairing exists. It would be correct for before-and-after measurements on the same sales territories.
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