Which statement best describes data integrity in this laboratory context?

• A. Inconsistent duplications in data
• B. Data must be deleted after use
• C. Maintaining consistent, accurate data
• D. Data integrity is unrelated to QA activities

Answer: (C)

Data integrity in the laboratory means keeping data complete, consistent, and accurate throughout its lifecycle—from collection to analysis to storage. This ensures that results truly reflect what was observed and can be trusted and traced back to the source. The best way to describe this is by emphasizing maintaining data that is consistent and accurate, so records remain reliable and verifiable over time. In practice, data should be recorded as observed, kept free from unauthorized alterations, and preserved for future review and audits. Concepts like ALCOA (attributable, legible, contemporaneous, original, accurate) capture these ideas and reinforce why accuracy and consistency are central to data integrity.

Inconsistent duplications introduce confusion and errors, undermining trust in the data. Deleting data after use removes the necessary audit trail that shows how results were obtained. And QA activities are specifically aimed at protecting data integrity—through proper documentation, calibration, review, and controls—not at disconnecting from it.