Unknown values in data quality evaluation should:

• A. be monitored through process controls
• B. be legitimately utilized
• C. have stable rates over time
• D. all of the above

Answer: (D)

Missing or unknown values in data quality evaluation should be actively managed in ways that ensure trustworthiness of analyses. Monitoring through process controls means putting in place data governance and automated checks so unknown entries are detected early and addressed, preventing unnoticed quality gaps. Legitimately utilized means handling missing data properly rather than ignoring it—documenting why values are unknown, applying appropriate imputation or analysis methods that accommodate missingness, and using missingness indicators when helpful. Having stable rates over time ensures that the frequency of unknown values doesn’t drift unpredictably; stability supports meaningful comparisons across periods and detects true changes rather than artifacts of data collection.

Together, these practices cover the essential ways unknown values should be handled, making all of the above appropriate.