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2. Measurement Process Characterization
2.5. Uncertainty analysis
2.5.3. Type A evaluations

Type A evaluations of bias

Sources of bias relate to the specific measurement environment 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:
  1. instruments
  2. operators
  3. geometries
  4. other
Calibrated instruments do not fall in this class 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.
The best strategy is to correct for bias and compute the uncertainty of the correction 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.
Only limited comparisons can be made among sources of possible bias 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.
Guidelines for treatment of biases 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
Plan for testing for assessing bias 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.

Sources of data for evaluating this type of bias Databases for evaluating bias may be available from:
Strategies for assessing corrections and uncertainties associated with significant biases

Type of bias
Type of correction
1. Inconsistent
Sign change (+ to -) Varying magnitude
Based on maximum bias
2. Consistent
Instrument bias ~ same magnitude over many artifacts
Bias (for a single instrument) = difference from average over several instruments
Standard deviation of correction
3. Not correctable because of sparse data - consistent or inconsistent
Limited testing; e.g., only 2 instruments, operators, configurations, etc.
Standard deviation of correction
4. Not correctable - consistent
Lack of resolution, non-linearity, drift,
material inhomogeneity
Based on maximum bias
Strategy for no significant bias If there is no significant bias over time, there is no correction and no contribution to uncertainty.
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