DA0-002 Data Analysis Practice Question
A data analyst wants to test if the proportion of customers who prefer Product A over Product B is different from 50%. She surveys 200 customers and finds that 120 prefer Product A. Which statistical test should she use?
⚠ Common exam trap
DA0-002 often tests test selection by scenario; candidates confuse proportion tests with chi-square or t-tests, especially when the word 'prefer' suggests a comparison between two products rather than a single proportion against a benchmark.
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
✓
One-sample z-test for proportions
The analyst wants to test whether the proportion of customers preferring Product A differs from 50%, using a single sample of 200 customers with 120 preferring A. This is a one-sample test of a proportion against a hypothesized value (0.5), so the one-sample z-test for proportions is appropriate. It compares the observed proportion (0.60) to the null hypothesis proportion (0.50).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Chi-square test of independence
Why it's wrong here
A chi-square test of independence compares association between two categorical variables across a contingency table, not a single proportion against a hypothesised value. It would be right if testing whether preference depends on, say, region or age group, where two categorical dimensions are cross-tabulated.
- ✓
One-sample z-test for proportions
Why this is correct
The scenario compares one observed sample proportion against a hypothesised population proportion of 0.50, with a large sample of 200. A one-sample z-test for proportions is the appropriate parametric test for this single-proportion hypothesis, unlike chi-square or two-sample alternatives.
- ✗
ANOVA
Why it's wrong here
ANOVA compares means across three or more groups by partitioning variance, so it cannot test a single proportion against 0.5. It would be correct when comparing mean scores, such as average spend, across several independent customer segments to detect any group-mean difference.
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Two-sample t-test
Why it's wrong here
A two-sample t-test compares means between two independent groups, requiring continuous outcome data, whereas this scenario has one binary outcome summarised as a proportion. It would be correct when comparing mean transaction values, for example, between customers of two different regions.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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