If a dataset contains missing values, which data quality issue is present?

• A. Duplication
• B. Redundancy
• C. Outliers
• D. Missing values

Answer: (D)

Missing values indicate incomplete data, which is a data quality issue affecting the dataset’s completeness. When some fields have no observed value, analyses can be biased or require special handling, such as imputation or removing those records. This is distinct from duplication (records repeated), redundancy (unnecessary repetition of data), and outliers (values that are unusually extreme). Recognizing missing values helps you decide how to handle incomplete records and maintain data integrity.