CITI Good Laboratory Behavior Practice Test

The key idea here is data integrity: records must be complete, accurate, and fully explainable so the study’s results can be trusted. When investigators review data submitted to the FDA, they look for anything that calls that trust into question. Inconsistent and/or unexplained entries signal that the data aren’t being recorded and reported transparently. They create doubt about whether observations were recorded correctly, whether edits were authorized, or whether data were fabricated or inappropriately altered. That kind of flag prompts investigators to dig deeper into the entire data set and the study conduct. Other issues like missing pages, outdated test methods, or unapproved methods are also problematic and can lead to noncompliance, but they don’t point to a data integrity problem as directly or as early as inconsistent or unexplained data entries. The consistency and justification of what was recorded is the first line of evidence that something in the data handling may be compromised.