Which error type can be difficult to detect and may not show immediate quantitative discrepancies?

Omission errors can be particularly challenging to detect because they involve the absence of necessary data or actions rather than a visible mistake or discrepancy. Unlike other types of errors, such as mechanical or quantitative errors, which may produce obvious effects that can be measured or observed, omission errors often go unnoticed until their impact is felt, usually after a significant amount of time. For instance, failing to include a crucial piece of information in a data set might not be immediately evident, making it difficult to identify and correct the issue promptly. This subtlety distinguishes omission errors from others, as their influence may only become apparent when analyzing outcomes that depend critically on complete data.