A record that fails quantitative analysis is missing which quality criterion?

Completeness is the quality criterion that ensures all necessary data is present within a record. In quantitative analysis, particularly when dealing with numerical data or statistical methods, missing data elements can significantly distort results and interpretations. A record lacking completeness may not provide a full picture of the situation being analyzed, thus compromising the accuracy and reliability of the results drawn from that data.

For instance, if a health record is missing key information such as a patient's medical history, test results, or demographic data, any analysis performed using that record could yield incomplete insights, potentially leading to misguided conclusions about patient care and outcomes. This underscores the importance of completeness as a foundational element in the integrity and usability of data.