DA0-002 Data Analysis Practice Question
A researcher is designing an A/B test to compare two website layouts. Which TWO elements are essential for determining the required sample size?
⚠ Common exam trap
DA0-002 often tests whether candidates confuse post-hoc statistics like p-value and sample mean with pre-experiment design inputs, so the trap is selecting observed outcomes instead of the design parameters power and effect size.
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
✓
Statistical power
Statistical power (B) is essential because it defines the probability of detecting a true effect when one exists, and standard sample-size formulas require a target power (typically 0.80) as an input. Desired effect size (D) is also essential because the minimum detectable or expected difference between the two layouts directly determines how many observations are needed—smaller effects require larger samples. Together with the significance level (alpha), power and effect size are the core parameters in sample-size calculations for A/B tests. The sample mean (A) is an outcome estimated from data, not an input for planning sample size. Confidence interval width (C) is a result that depends on sample size rather than a prerequisite for computing it. The p-value (E) is also a post-hoc result of the test, not a design parameter used to determine required sample size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sample mean
Why it's wrong here
The sample mean is measured from collected data, so it cannot determine a sample size chosen beforehand. It is tempting because means are central to A/B comparison, but the calculation needs the expected difference between layouts, the standard deviation, significance level and power instead.
- ✓
Statistical power
Why this is correct
Statistical power, typically 80%, is the probability of detecting a real difference if one exists. It directly determines sample size: higher power requires more observations, satisfying the stem's requirement for an essential input alongside effect size and significance level.
- ✗
Confidence interval width
Why it's wrong here
Confidence interval width describes the precision of an estimate after data collection, not an input to sample-size calculation. It is tempting because narrower intervals and larger samples are related, but the formula takes effect size, power, significance level and variance; interval width is derived from those, not supplied.
- ✓
Desired effect size
Why this is correct
Effect size quantifies the minimum difference between layouts you want to detect. Smaller effects demand larger samples, so specifying it directly drives the sample size calculation alongside power and significance level, satisfying the stem's requirement for essential inputs.
- ✗
P-value
Why it's wrong here
The p-value is the outcome threshold used after testing to judge significance; it does not enter the sample-size formula. It is tempting because significance level (alpha) does, and the p-value is the closest familiar concept, but the calculation requires effect size, power, alpha and variance instead.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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