These data quality objectives need to be reported:
1. Analytical bias (say <2.5%)
2. Coefficient of variation (say<5%)
3. Detection of critical systematic error (say >90%)
4. False rejection rate (say <5%)
Data quality objectives are realistic operating specifications giving allowable levels of inaccuracy (and imprecision) for different grades of material, and for each process related analytical or operating quality requirement. Objectives have to be set and must take into account cost implications if limits are set too tight or too loose. These will result in too many QC failures (true and false). There must be realistically high probabilities for error detection and realistically low probabilities for false alerts. Primary and secondary laboratories have to be checked for compatible equipment, methods and detection limits and obviously, the success of the program relies on having appropriate grade and matrix control materials.