MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with missing values in several columns. The dataset contains customer demographic information and purchase history. Which approach should the data scientist take to handle missing values without introducing bias into the dataset?
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
✓
Use multiple imputation to estimate missing values.
Multiple imputation produces multiple estimates of missing values, accounting for the uncertainty in the imputation and reducing bias compared to simpler methods. Option A is wrong because dropping all rows with missing values can lead to loss of data and potential bias if missingness is not completely random. Option B is wrong because mean imputation can underestimate variance and distort relationships between variables. Option C is wrong because replacing missing values with a constant (e.g., 0) introduces arbitrary values that can skew the data distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Drop all rows with any missing values.
Why it's wrong here
Dropping rows can introduce bias if missingness is not random.
- ✗
Impute missing values with the mean of each column.
Why it's wrong here
Mean imputation reduces variance and can bias relationships.
- ✗
Replace missing values with a constant, such as 0.
Why it's wrong here
Using a constant value is arbitrary and can distort the data.
- ✓
Use multiple imputation to estimate missing values.
Why this is correct
Multiple imputation accounts for uncertainty and reduces bias.
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