Data quality: Which data term describes non-sensical values in an attribute like a street name in a gender field?

Data quality: Which data term describes non-sensical values in an attribute like a street name in a gender field?

• A. Outliers
• B. Duplicate records
• C. Missing values
• D. Non-sensical values in a field

Answer: (D)

In data quality, the key idea is whether a value belongs to the expected domain for that attribute. If an attribute is supposed to capture gender, the values should be the defined categories for gender. When a value is something like a street name instead of a gender category, it doesn't make sense for that field and indicates an invalid or nonsensical entry for that attribute. This is exactly what the option describes: a non-sensical value in a field, which signals a data quality issue that needs validation, correction, or cleansing. This differs from outliers (which are unusual numeric values within a measurement), missing values (absent data), or duplicate records (repeated entries).

In data quality, the key idea is whether a value belongs to the expected domain for that attribute. If an attribute is supposed to capture gender, the values should be the defined categories for gender. When a value is something like a street name instead of a gender category, it doesn't make sense for that field and indicates an invalid or nonsensical entry for that attribute. This is exactly what the option describes: a non-sensical value in a field, which signals a data quality issue that needs validation, correction, or cleansing.

This differs from outliers (which are unusual numeric values within a measurement), missing values (absent data), or duplicate records (repeated entries).