DA0-002 Data Analysis Practice Question
A data analyst is testing whether the average sales amount differs between two regions. Which statistical test is most appropriate?
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
✓
Two-sample t-test
A two-sample t-test compares the means of two independent groups.
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
Why it's wrong here
Chi-square tests association between categorical variables using frequency counts, so it cannot compare mean sales amounts, which are continuous. It tempts analysts because both regions could be treated as categories, but it would be correct when testing whether region and a categorical outcome such as purchase status are independent.
- ✗
ANOVA
Why it's wrong here
ANOVA compares means across three or more groups, so with only two regions it wastes the F-test framework where a two-sample t-test suffices. It tempts analysts because it generalises mean comparison, and it would be the correct choice when testing average sales across three or more regions simultaneously.
- ✓
Two-sample t-test
Why this is correct
A two-sample t-test compares the means of two independent groups, matching the two regions and continuous sales measure. It tests whether the difference in average sales is statistically significant, unlike a paired test, which requires matched observations.
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Paired t-test
Why it's wrong here
A paired t-test requires matched observations from the same subjects, such as before-and-after measurements, but the two regions contain independent samples. It tempts analysts because it compares two means, yet it would be correct when each sales figure in one region is paired with a corresponding figure in the other.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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