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2. Measurement Process Characterization
2.5. Uncertainty analysis


Type A evaluations

Type A evaluations apply to both error and bias Type A evaluations can apply to both random error and bias. The only requirement is that the calculation of the uncertainty component be based on a statistical analysis of data. The distinction to keep in mind with regard to random error and bias is that:
  • random errors cannot be corrected
  • biases can, theoretically at least, be corrected or eliminated from the result.
Caveat for biases The ISO guidelines are based on the assumption that all biases are corrected and that the only uncertainty from this source is the uncertainty of the correction. The section on type A evaluations of bias gives guidance on how to assess, correct and calculate uncertainties related to bias.
Random error and bias require different types of analyses How the source of error affects the reported value and the context for the uncertainty determines whether an analysis of random error or bias is appropriate.

Consider a laboratory with several instruments that can reasonably be assumed to be representative of all similar instruments. Then the differences among these instruments can be considered to be a random effect if the uncertainty statement is intended to apply to the result of any instrument, selected at random, from this batch.

If, on the other hand, the uncertainty statement is intended to apply to one specific instrument, then the bias of this instrument relative to the group is the component of interest.

The following pages outline methods for type A evaluations of:
  1. Random errors
  2. Bias
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