The reliability and usefulness of data obtained from any analytical or measurement procedure depends on the quality of those data. Data quality depends, in turn, on the accuracy and precision of the data set.

Quality assurance is a set of operating principles through which data quality can be determined and defended. Quality assurance begins with a quality control plan, which delineates responsibilities, provides sample control and documentation procedures, specifies analytical methods, calibration techniques, standardization methods, and equipment maintenance routines, and prescribes data assessment, reduction, and reporting procedures.

Quality control procedures may include both internal and external components. External components can include training and certification procedures, competence testing, analysis of externally supplied standards and unknowns, and external data review. Internal procedures include recovery of known additions, calibration with standards, analysis of reagent blanks, analysis of duplicates (in addition to real replication), and determination of precision.

All of this may sound tedious, especially when the typical student's laboratory experience has included only "cookbook" lab procedures. However, proper attention to QA/QC guidelines reduces the waste and frustration of untrustworthy results. Moreover, this is how the "real world" does things.

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