For ensuring data integrity, QA/QC activities should be applied ...

For ensuring data integrity, QA/QC activities should be applied ...

• A. Throughout all phases
• B. Only during data collection
• C. Only during reporting
• D. Only during design

Answer: (A)

The main idea being tested is that data integrity requires QA/QC activities to be integrated across the entire project lifecycle, not just at a single point. Data integrity means the data are accurate, complete, consistent, and traceable from collection through processing, analysis, reporting, and storage. If QA/QC is built in only during data collection, or only during reporting, or only during design, errors and biases that occur at other stages can go unnoticed and affect conclusions. Embedding QA/QC at all stages—planning and design, field data collection with proper calibration and chain of custody, data processing with validation and version control, analysis with transparent protocols, and reporting with audit trails and reproducibility checks—helps ensure any change, discrepancy, or mistake is detected and corrected early. This continuous oversight preserves data quality, reproducibility, and trust in the results.

The main idea being tested is that data integrity requires QA/QC activities to be integrated across the entire project lifecycle, not just at a single point. Data integrity means the data are accurate, complete, consistent, and traceable from collection through processing, analysis, reporting, and storage. If QA/QC is built in only during data collection, or only during reporting, or only during design, errors and biases that occur at other stages can go unnoticed and affect conclusions. Embedding QA/QC at all stages—planning and design, field data collection with proper calibration and chain of custody, data processing with validation and version control, analysis with transparent protocols, and reporting with audit trails and reproducibility checks—helps ensure any change, discrepancy, or mistake is detected and corrected early. This continuous oversight preserves data quality, reproducibility, and trust in the results.