2.
Measurement Process Characterization
2.5.
Uncertainty analysis
2.5.3.
Type A evaluations
2.5.3.3.
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Type A evaluations of bias
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Sources of bias relate to the specific measurement environment
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The sources of bias discussed on this page cover specific measurement
configurations. Measurements on test items are usually made on a
single day, with a single operator, with a single instrument, etc.
Even if the intent of the uncertainty is to characterize only those
measurements made in one specific configuration, the uncertainty
must account for any significant differences due to:
- instruments
- operators
- geometries
- other
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Calibrated instruments do not fall in this class
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Calibrated instruments do not normally fall in this class because
uncertainties associated with the instrument's calibration are reported
as type B evaluations, and the instruments in the laboratory should
agree within the calibration uncertainties. Instruments whose responses
are not directly calibrated to the defined unit are candidates for
type A evaluations. This covers situations where the measurement is
defined by a test procedure or standard practice using a specific
instrument type.
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The best strategy is to correct for bias and compute the uncertainty
of the correction
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This problem was treated on the foregoing page as an
analysis of random error for the case where
the uncertainty was intended to apply to all measurements for all
configurations. If measurements for only one configuration are
of interest, such as measurements made with a specific instrument, or
if a smaller uncertainty is required, the differences among, say,
instruments are treated as biases. The best strategy in this
situation is to correct all measurements made with a specific
instrument to the average for the instruments in the laboratory and
compute a type A uncertainty for the correction. This strategy, of
course, relies on the assumption that the instruments in the laboratory
represent a random sample of all instruments of a specific type.
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Only limited comparisons can be made among sources of
possible bias
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However, suppose that it is possible to make comparisons among, say,
only two instruments and neither is known to be 'unbiased'. This
scenario requires a different strategy because the average will not
necessarily be an unbiased result. The best strategy if there is a
significant difference between the instruments, and this should be
tested, is to apply a 'zero' correction and assess a type A uncertainty
of the correction.
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Guidelines for treatment of biases
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The discussion above is intended to point out that there are many
possible scenarios for biases and that they should be treated on a
case-by-case basis. A plan is needed for:
- gathering data
- testing for bias (graphically and/or statistically)
- estimating biases
- assessing uncertainties associated with significant biases.
caused by:
- instruments
- operators
- configurations, geometries, etc.
- inhomogeneities
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Plan for testing for assessing bias
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Measurements needed for assessing biases among
instruments, say, requires a random sample of I (I > 1)
instruments from those available and measurements on Q (Q >2)
artifacts with each instrument. The same can be said for the other
sources of possible bias. General strategies for dealing with
significant biases are given in the table below.
Data collection and analysis for assessing biases related to:
are addressed in the section on gauge
studies.
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Sources of data for evaluating this type of bias
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Databases for evaluating bias may be available from:
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