DA0-002 Data Analysis Practice Question
Which TWO of the following are commonly used techniques for handling missing data in a dataset? (Select TWO).
⚠ Common exam trap
CompTIA often tests the distinction between common imputation methods (mean, median, mode, regression) and data removal techniques, trapping candidates who confuse 'dropping rows' as a primary technique when imputation is more widely recommended for preserving data integrity.
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
✓
Mean imputation
Mean imputation is a commonly used technique for handling missing numerical data where the missing value is replaced with the mean of the observed values for that feature. It preserves the sample size and is simple to implement, though it can reduce variance and distort relationships if data is not missing completely at random.
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 this is correct
Mean imputation replaces missing values with the mean of the column.
- ✗
Mode imputation
Why it's wrong here
Mode imputation is for categorical data, not typically for numerical.
- ✗
Dropping columns with missing data
Why it's wrong here
Dropping columns is a technique but often loses data; it's not a common imputation technique.
- ✗
Dropping rows with missing data
Why it's wrong here
Dropping rows is a technique but not imputation; it's deletion.
- ✓
Regression imputation
Why this is correct
Regression imputation uses a regression model to predict missing values.
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