Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
procedures are subject to a variety of analytical inaccuracies or
biases. Fig. 31B shows the effects of a proportional bias in which
the reported values are higher than the expected values. This bias is
called proportional because the amount of bias increases in direct
proportion to the concentration of analyte in the specimen. Fig. 31C
illustrates the effect of a constant bias, in which the reported
values are higher than the expected values by a constant amount, at
all concentrations of analyte. The biases shown in Figs. 31B and 31C
are positive biases, because the reported values are greater than the
expected values. Of course, negative biases may also occur;
furthermore, the reported values may fall along a curve rather than a
straight line. Fig. 31D illustrates combined positive and negative
proportion biases. When combined biases are present, there is
frequently one concentration at which the reported value corresponds
to the expected value. This phenomenon -- that the reported value at
some analyte concentration is the same as the expected value whereas
there is disagreement at all other concentrations -- is commonly
observed and must be considered when interpreting quality control
data. Because of this, it is recommended that reference samples should
cover the range of expected measurement values.
11.5.1.2 Precision
Precision or reliability of an analysis refers to the uniformity
of the results of replicate analyses irrespective of the true
concentration of the analyte. The precision of an analysis may vary
depending on many factors, such as the skill and experience of the
analyst, the purity of the chemicals, the quality of measuring devices
and the time interval between replicate analyses. The precision is
determined by calculating the percentage relative standard deviation
among replicate analyses. In this manner precision can be defined as
the closeness of agreement between independent test results obtained
under prescribed conditions.
The effects of random variation on analyses are illustrated in
Figs. 32A and 32B, which show how laboratory's results may, on the
average, fall along some operational line even though the individual
results are distributed about the line, within certain limits of
variability. Fig. 32A illustrates limits of variability that increases
in proportion to the mean analyte concentration.
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