How should data integrity issues be handled?

How should data integrity issues be handled?

• A. Fabricate data if it helps study outcomes
• B. Modify data values to fit hypotheses
• C. Never fabricate data and maintain integrity
• D. Alter data with supervisor consent

Answer: (C)

Data integrity means all trial data are accurate, complete, and verifiable; fabricating data or altering values undermines that integrity and jeopardizes participant safety, scientific validity, and regulatory compliance. The proper approach is to never fabricate data and to maintain integrity by following established data management practices: protect source data, capture information accurately, and preserve an audit trail of all edits or corrections. If discrepancies or errors are found, report them through formal procedures, document the原因 and rationale, and apply corrections only through approved processes that preserve traceability. Any changes should be defensible, well-documented, and reviewed, rather than done informally or to fit a hypothesis. In practice, adhere to principles like ALCOA (Attributable, Legible, Contemporary, Original, Accurate) and applicable regulations, so that data remain trustworthy and credible. This is why altering data with supervisor consent is not acceptable; legitimate corrections require proper, auditable procedures rather than ad-hoc changes.

Data integrity means all trial data are accurate, complete, and verifiable; fabricating data or altering values undermines that integrity and jeopardizes participant safety, scientific validity, and regulatory compliance. The proper approach is to never fabricate data and to maintain integrity by following established data management practices: protect source data, capture information accurately, and preserve an audit trail of all edits or corrections. If discrepancies or errors are found, report them through formal procedures, document the原因 and rationale, and apply corrections only through approved processes that preserve traceability. Any changes should be defensible, well-documented, and reviewed, rather than done informally or to fit a hypothesis. In practice, adhere to principles like ALCOA (Attributable, Legible, Contemporary, Original, Accurate) and applicable regulations, so that data remain trustworthy and credible. This is why altering data with supervisor consent is not acceptable; legitimate corrections require proper, auditable procedures rather than ad-hoc changes.