DA0-002 Data Analysis Practice Question
In A/B testing, which factor is increased by having a larger sample size?
⚠ Common exam trap
The trap is conflating p-value with power or thinking that larger samples increase effect size; candidates often pick p-value because they associate it with significance, but the exam expects understanding that power is the probability of detecting a true effect and is directly boosted by sample 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 is the probability of correctly rejecting a false null hypothesis (i.e., detecting a true effect). Increasing sample size reduces the standard error, making it easier to detect a true effect and thus increasing power. This is a fundamental principle in hypothesis testing and A/B testing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
P-value
Why it's wrong here
The p-value is a computed result from the observed data, not a factor increased by sample size; larger samples tend to produce smaller p-values for a genuine effect. Sample size is increased to raise statistical power and narrow confidence intervals, not to inflate the p-value itself.
- ✗
Effect size
Why it's wrong here
Larger samples increase statistical power and precision of the estimate, not the effect size, which is a property of the underlying population difference. Effect size is what you would calculate to determine the sample size needed for a study, making it tempting when reasoning about A/B test design.
- ✗
Type I error rate
Why it's wrong here
A larger sample size reduces the Type I error rate's practical impact by shrinking standard error, but the significance threshold alpha itself is unchanged; Type I error is controlled by the chosen alpha, not sample size. Increasing sample size is correct when the goal is greater statistical power to detect a real effect.
- ✓
Statistical power
Why this is correct
Larger samples shrink the standard error of the estimated effect, which directly raises statistical power — the probability of detecting a true difference when one exists. This satisfies the scenario's need to distinguish genuine treatment effects from random variation.
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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
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