The correct answer is that the two samples must be independent of each other, and the data in each group must be approximately normally distributed. Independence means the observations in one sample are not related to or influenced by those in the other sample—for example, sales from Store A should have no connection to sales from Store B. The normality assumption ensures that the sampling distribution of the mean difference is well-behaved, which is critical for calculating valid p-values; while the Central Limit Theorem can relax this for large samples (n > 30), smaller samples require the data to be roughly bell-shaped. On the CompTIA Data+ DA0-001 exam, this question tests your grasp of parametric test prerequisites, often appearing alongside a scenario comparing two groups, with a common trap being to confuse independence with equal variance or to assume normality is always optional. A useful memory tip: think “I-N” for Independence and Normality—the two pillars of a valid two-sample t-test.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
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
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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).
Detailed technical explanation
How to think about this question
The two-sample t-test relies on the assumption of independence between groups and approximate normality of the sampling distribution of the difference in means. Under the hood, the test statistic follows a t-distribution with degrees of freedom calculated via the Welch–Satterthwaite equation when variances are unequal, which adjusts for heteroscedasticity. In real-world A/B testing for store sales, analysts often use Welch's t-test by default because store variances frequently differ due to factors like foot traffic or local demographics.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The data is approximately normally distributed — Option C is correct because 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.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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