If you cannot impute missing data in an LDS task, which action is appropriate?

If you cannot impute missing data in an LDS task, which action is appropriate?

• A. Exclude the affected records from the analysis.
• B. Accurately replace missing values with the mean without justification.
• C. Ignore missing data and proceed with full dataset.
• D. Create synthetic data to replace missing values without justification.

Answer: (A)

Missing data can distort results if you can’t justify a method to fill them in. If you don’t have a defensible imputation approach, the safest and most appropriate action is to remove the records that contain missing values. This keeps the analysis based on complete, reliable data and avoids introducing unverified assumptions that could bias conclusions. Replacing missing values with a simple average can mislead by reducing variability and weakening relationships between variables, and ignoring missing data or fabricating data would also bias results or undermine trust in the analysis. Dropping the problematic records preserves the integrity of your findings, even though it may reduce the sample size.

Missing data can distort results if you can’t justify a method to fill them in. If you don’t have a defensible imputation approach, the safest and most appropriate action is to remove the records that contain missing values. This keeps the analysis based on complete, reliable data and avoids introducing unverified assumptions that could bias conclusions.

Replacing missing values with a simple average can mislead by reducing variability and weakening relationships between variables, and ignoring missing data or fabricating data would also bias results or undermine trust in the analysis. Dropping the problematic records preserves the integrity of your findings, even though it may reduce the sample size.