DA0-002 Data Analysis Practice Question
An analyst is planning an A/B test to compare two website designs. Which TWO factors should be considered when calculating the required sample size?
⚠ Common exam trap
DA0-002 often tests the four inputs to sample size (α, power, effect size, variance) — candidates pick data type or missing values because they sound statistical, but only effect size and power are among the core parameters.
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
✓
Desired effect size
The required sample size for an A/B test depends directly on the desired effect size (B), because smaller effects are harder to detect and demand more observations to distinguish a real difference from noise. It also depends on statistical power (C), conventionally set at 0.80, since higher power (lower Type II error risk) requires a larger sample to reliably detect the effect when it truly exists. The data type of the outcome variable (A) affects the choice of statistical test, not the sample-size formula's core inputs, and the color scheme (D) is merely the design variation being tested, not a computational factor. The number of missing values (E) is a data-quality issue handled during cleaning or imputation and is not a standard parameter in sample-size calculation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data type of the outcome variable
Why it's wrong here
Sample size formulae depend on the effect size metric, significance level and power; the outcome's data type only determines which test is used, not the required n. It tempts because choosing a test precedes power analysis, but the stem asks for sample-size factors specifically.
- ✓
Desired effect size
Why this is correct
The desired effect size is the minimum detectable difference between designs that the test must reliably detect. Smaller effects require substantially larger samples, making it a core input to any sample size calculation for the A/B test.
- ✓
Statistical power
Why this is correct
Statistical power (typically 80%) determines the probability of detecting a genuine difference between the two designs if one exists. Higher power demands a larger sample, so it directly drives the required size calculation alongside significance level and effect size.
- ✗
Color scheme of the designs
Why it's wrong here
Colour scheme is the treatment being tested, not an input to the power calculation; it cannot change the required sample size. It tempts because it is central to the A/B test design itself, but sample size depends on baseline rate, minimum detectable effect, alpha and power.
- ✗
Number of missing values
Why it's wrong here
Missing values affect data cleaning and analysis validity, not the statistical inputs of a power calculation. It tempts because data quality matters generally in experiments, but sample size depends on baseline conversion rate, minimum detectable effect, significance level and power, none of which involve missingness.
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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.