Humans face uncertainties every moment of every day. But what does “measurement uncertainty” (MU) mean for laboratorians? Anders Kallner, MD, PhD, an associate professor in the department of clinical chemistry at Karolinska University Hospital in Stockholm, Sweden, provided some answers in CLN’s June Ask the Expert column.
“As soon as we move into the laboratory and start quantifying various properties of our samples, we also move into metrology, which is the science of measurement and its applications ... MU is a universal concept in metrology. It is defined as an attempt to identify all events in a measurement procedure that could cause a dispersion of the results, quantify the dispersion, and summarize it in one expression known as the combined uncertainty,” Kallner explains.
Following are additional excerpts from his interview with CLN:
Is MU a well-recognized concept?
MU was launched in 1993 by the Bureau International des Poids et Mesures in close collaboration with other prestigious international professional organizations. MU is thus firmly established and recognized globally, and not only in chemistry. The official publication on MU, “Guide to the expression of uncertainty in measurement”(GUM), is freely available on the Internet.
Does MU add anything to present practice?
MU is another way of combining systematic and random errors. Whereas the (total error) TE approach includes bias in its calculations, a central tenet of the MU approach is that if the bias is known and of importance, then the lab should eliminate or minimize it instead.
How can the uncertainty be estimated?
The standard uncertainty of each step in the measurement procedure can be estimated by repeated measurements (Type A) or, if this is impossible, by professional experience (Type B). The standard uncertainties of all the individual steps are then combined using error propagation rules in what is known as the “bottom-up” method.
So what is the main takeaway here?
Don’t underestimate the importance of identifying and quantifying sources of uncertainty. It helps the laboratorian to identify root causes of errors and is a systematic way to improve testing methods.
Pick up the June issue of CLN to learn more about Kallner’s views on MU.