DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset and finds that the 'age' column has several missing values. Which method of handling missing values is least likely to introduce bias if the missingness is completely at random?
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
✓
Listwise deletion
Listwise deletion (removing rows with missing values) is simple and unbiased if data is MCAR, but it reduces sample size. However, it is least likely to introduce bias among the options when MCAR holds.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Mean imputation
Why it's wrong here
Mean imputation can distort relationships and reduce variance.
- ✓
Listwise deletion
Why this is correct
If MCAR, listwise deletion gives unbiased estimates, though with less power.
- ✗
Mode imputation
Why it's wrong here
Mode imputation can create bias by overrepresenting the most common value.
- ✗
Forward-fill
Why it's wrong here
Forward-fill is for time series and can introduce bias.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.