How are accuracy and precision defined in QA/QC, and how are they evaluated in the lab?

How are accuracy and precision defined in QA/QC, and how are they evaluated in the lab?

• A. Accuracy is measurement speed; precision relates to instrument noise; evaluated by energy usage.
• B. Accuracy refers to reproducibility between labs; precision refers to bias; evaluated by spike tests only.
• C. Accuracy is the closeness to the true value; precision is the repeatability of results; evaluated with recoveries, duplicates, and control charts.
• D. Accuracy is the stability over time; precision is calibration accuracy; evaluated by calibration curves.

Answer: (C)

Accuracy is how close a measured value is to the true value, while precision is how repeatable or consistent those measurements are. In the lab, you assess accuracy by checking how close results are to a known true value, often using spike recoveries or reference materials. For example, adding a known amount of analyte to a sample and measuring how much you actually recover tells you how bias-free your method is; percent recovery near 100% indicates good accuracy. Precision is about repeatability. You evaluate it by performing multiple measurements of the same sample and looking at the spread of results. Small variation means high precision. This is often seen with duplicate or triplicate analyses and is quantified with stats like standard deviation or relative standard deviation. Control charts are also used to monitor precision over time, showing whether the process stays consistent or drifts. So, a lab checks accuracy with recoveries and known references to gauge closeness to the true value, and checks precision with duplicates and control charts to gauge consistency across runs and over time.

Accuracy is how close a measured value is to the true value, while precision is how repeatable or consistent those measurements are. In the lab, you assess accuracy by checking how close results are to a known true value, often using spike recoveries or reference materials. For example, adding a known amount of analyte to a sample and measuring how much you actually recover tells you how bias-free your method is; percent recovery near 100% indicates good accuracy.

Precision is about repeatability. You evaluate it by performing multiple measurements of the same sample and looking at the spread of results. Small variation means high precision. This is often seen with duplicate or triplicate analyses and is quantified with stats like standard deviation or relative standard deviation. Control charts are also used to monitor precision over time, showing whether the process stays consistent or drifts.

So, a lab checks accuracy with recoveries and known references to gauge closeness to the true value, and checks precision with duplicates and control charts to gauge consistency across runs and over time.