A data analyst pulls a table similar to the following one: Which of the following best explains the data issue with TypeID?

Redundancy in data refers to the presence of duplicate data in multiple places or when information can be inferred from other stored data, which often indicates a database design flaw. If TypeID should uniquely identify a Type, but shows multiple entries for the same Type or inconsistent TypeID-to-Type mappings, this indicates redundant information.

An outlier is a data point significantly different from other observations, which is not the primary issue described by an inconsistent or repeated TypeID mapping.

Missing data implies an absence of values, not an issue of repeated or inconsistently mapped existing values in TypeID.

While duplication can be a form of redundancy, 'redundancy' is a broader term encompassing semantic repetition or information that is unnecessarily stored, often due to a poor data model, which better describes inconsistent TypeID mappings.