CITI Good Laboratory Behavior Practice Test

Data integrity in GLP means data are accurate, complete, and verifiable, so they can be reviewed and audited. In practice, this means records are attributable to the person who generated them, legible, contemporaneous with the observation, original or a true copy, and accurate. The systems used to collect and store data should preserve their authenticity over time, with proper change control and traceability so an auditor can confirm what happened and when. The idea that data may be approximate if the study design is robust misses the core point: integrity requires data truly reflect what was observed, not a best-guess version. Robust design does not excuse inaccuracy or incompleteness. Likewise, it’s not acceptable to limit the expectation of integrity to final report data; regulators and QA expect raw data and source documents to be complete and trustworthy, because the final conclusions depend on the entire data trail. Finally, data integrity isn’t optional because QA is strong—QA supports integrity, but it cannot substitute for proper data capture, retention, and traceability across the study.