DA0-002 Data Analysis Practice Question
A data analyst is performing hypothesis testing to compare the mean sales of two store locations. Which TWO conditions must be satisfied to use a two‑sample t‑test? (Select TWO.)
⚠ Common exam trap
CompTIA often tests the misconception that equal sample sizes or equal variances are required for a two-sample t-test, but the actual core assumptions are independence and normality (or large sample sizes via CLT).
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
✓
The data is approximately normally distributed
The two-sample t-test assumes that the data in each group are approximately normally distributed. This is a key parametric assumption; if the sample sizes are large (typically n > 30), the Central Limit Theorem can relax this requirement, but for smaller samples, normality must hold to ensure valid test statistics and p-values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The data is paired between the two locations
Why it's wrong here
Paired data would require a paired t-test, not a two-sample t-test.
- ✗
The sample sizes are equal
Why it's wrong here
Equal sample sizes are not a required condition for the two-sample t-test.
- ✓
The data is approximately normally distributed
Why this is correct
Normality is assumed for the t-test, though it is robust for large samples.
- ✗
The variances of the two populations are equal
Why it's wrong here
Equal variances are required only for the pooled t-test; Welch's t-test does not require this assumption.
- ✓
The two samples are independent of each other
Why this is correct
Independence is a key assumption for the two-sample t-test.
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