Which term describes data that does not yield accurate results due to inconsistencies?

The term that describes data yielding inaccurate results due to inconsistencies is associated with data comparability issues. This concept refers to problems that arise when data from different sources or formats cannot be effectively aligned or compared due to inconsistencies in definitions, units of measurement, or underlying assumptions. When data lacks comparability, it can produce misleading insights, as it may not truly reflect the same phenomena or conditions across different datasets.

In contrast, data insufficiency pertains to a lack of enough data to draw meaningful conclusions. Data incompleteness typically refers to datasets that are missing essential information or attributes, rather than inconsistencies. Data redundancy involves having unnecessary repetition of data, which can lead to storage inefficiencies but does not directly relate to inaccuracies caused by inconsistency. Therefore, data comparability issues most accurately address the problems that arise when discrepancies across datasets lead to unreliable results.