DA0-002 Data Analysis Practice Question
A data scientist is conducting an A/B test with a significance level of 0.05. Which three factors should be considered when calculating the required sample size? (Choose THREE)
⚠ Common exam trap
The trap is including operational or data-context factors (seasonality, clustering hyperparameters) that feel relevant to experiment design but are not inputs to the statistical power calculation — only power, effect size, and α are.
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 (e.g., 0.80)
Option B is correct because statistical power (typically 0.80 or 80%) directly determines sample size: higher power requires a larger sample to reliably detect a true effect if one exists. Option C is correct because the minimum detectable effect size is a core input to sample-size formulas (e.g., n ≈ 16σ²/Δ² for a two-sample t-test at α=0.05, power=0.80); smaller effects demand much larger samples. Option E is correct because the significance level α (here 0.05) sets the Type I error threshold and appears in every sample-size calculation, with smaller α requiring larger samples. Option A is not a direct input to the standard sample-size formula, though seasonality may inform variance estimates or test design. Option D is unrelated: the number of clusters in k-means is an unsupervised clustering parameter and has no bearing on A/B test sample-size determination.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Seasonality of the data
Why it's wrong here
Seasonality affects when and how data is collected, not the arithmetic inputs of a sample-size formula. It is tempting because seasonal variation can inflate variance, and would be handled through stratification or blocking in the design, but the calculation itself uses effect size, power, alpha and variance.
- ✓
Statistical power (e.g., 0.80)
Why this is correct
Statistical power, typically 0.80, fixes the tolerated Type II error rate and determines how large a sample is needed to detect a real effect. It is a required input alongside significance level and effect size.
- ✓
Minimum detectable effect size
Why this is correct
The minimum detectable effect size directly drives required sample size: smaller effects demand larger samples to detect at the 0.05 significance level. It satisfies the stem's constraint by quantifying the smallest true difference the A/B test must reliably identify, alongside power and variance.
- ✗
Number of clusters in k-means
Why it's wrong here
Cluster count is a hyperparameter chosen when configuring k-means, not an input to sample-size calculation. It is tempting because clustering is a common analysis step, and would matter when tuning segmentation models, but sample size depends on effect size, power, variance and significance level.
- ✓
Significance level (α)
Why this is correct
The significance level α sets the tolerated Type I error rate and directly determines the critical value used in the sample size formula. Lowering α widens the required sample, so the stated 0.05 threshold must be an explicit input.
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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.