The meaning of the bias uncertainty measure

Characterization of measurement uncertainty in terms of root sums of squares of both unknown systematic as well as random error components is given meaning in the sense of prediction intervals. Both types of errors are commonly encountered with industrial hygiene air monitoring of hazardous substances. Two extreme types of measurement methods are presented for illustrating how confidence levels may be ascribed to prediction intervals defined by such uncertainty values. In the case of method calibration at each measurement, systematic error or bias may enter from a biased calibrant. At another extreme, a single initial method evaluation may leave residual bias owing to random error in the evaluation itself or to the use of a biased reference method. Analysis is simplified through new simple approximations to probabilistic limits (quantiles) on the magnitude of a non-central Student t-distributed random variable. Connection is established between traditional confidence limits, accuracy measures in the case of bias minimization and an uncertainty measure.