DA0-002 Data Analysis Practice Question
A retailer wants to test if a new website layout increases the average time spent on the site. They split traffic: control group (old layout) and treatment group (new layout). Which statistical test is most appropriate to compare the average time spent between the two groups?
⚠ Common exam trap
The trap is confusing the two-sample t-test with the paired t-test — candidates must check whether the two groups are independent (different subjects) or paired (same subjects measured twice), since that determines which t-test applies.
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
The question compares the means of a continuous outcome (average time spent) between two independent groups (control vs. treatment), which is exactly the two-sample t-test's purpose. The two-sample t-test evaluates whether the difference in group means is statistically significant, assuming approximately normal distributions or sufficiently large samples. It is the standard test for a two-group A/B comparison on a continuous metric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ANOVA
Why it's wrong here
ANOVA compares means across three or more groups; with only control and treatment, a two-sample t-test is the direct fit. ANOVA is tempting because it generalises mean comparison, and would be correct if the retailer tested several layout variants simultaneously rather than two.
- ✗
Pearson correlation
Why it's wrong here
Pearson correlation measures linear association between two continuous variables, not differences between group means. It suits testing whether time spent and pages viewed move together. Comparing control and treatment averages requires a two-sample t-test, which assesses mean difference across independent groups.
- ✗
Chi-square test
Why it's wrong here
Chi-square tests association between categorical variables, such as conversion counts by layout, not continuous means like time spent. It fits testing whether layout and purchase outcome are independent. Comparing average time between two groups needs a t-test on continuous data.
- ✓
Two-sample t-test
Why this is correct
A two-sample t-test compares the means of two independent groups, matching the control and treatment split. It tests whether the difference in average time spent is statistically significant, which is exactly the retailer's question about whether the new layout changes the mean.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.