DA0-002 Data Analysis Practice Question
A data analyst is comparing the means of two independent groups using a t-test. The sample sizes are small and the data is not normally distributed. Which condition is violated for a valid t-test?
⚠ Common exam trap
DA0-002 often tests the confusion between the assumptions of a t-test, leading candidates to select 'equal variances' or 'sample size larger than 30' when the actual violated condition is normality due to small, non-normal samples.
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
✓
Normality
A t-test assumes that the sampling distribution of the mean is approximately normal. With small sample sizes, the Central Limit Theorem does not guarantee normality, so the data itself should be approximately normal. Since the data is not normally distributed and sample sizes are small, the normality assumption is violated, making the t-test invalid.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Normality
Why this is correct
The t-test assumes the sampling distribution of the mean is normal. With small samples, the central limit theorem does not apply, so non-normal data violates the normality assumption. Independence and equal variances are separate assumptions; normality is the condition breached here.
- ✗
Equal variances
Why it's wrong here
Equal variances is not the violated condition here; Welch's t-test handles unequal variances, and the stem specifies small samples with non-normal data. It is tempting because variance homogeneity is a classic t-test assumption, and would be correct to check when choosing between pooled and Welch variants.
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Independence of observations
Why it's wrong here
Independence concerns how observations are sampled, not distribution shape; the stem's small, non-normal samples breach the normality assumption instead. It is tempting because independence underpins most parametric tests, and would be correct to flag when repeated measures or clustered data are collected.
- ✗
Sample size larger than 30
Why it's wrong here
A sample larger than 30 is a condition that supports normality assumptions, so its absence is not the violation described; small samples with non-normal data breach the t-test's normality requirement. The option is tempting because n>30 invokes the central limit theorem, which would justify a t-test in a different scenario.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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