Abstract
Objective data evaluation procedures involve the use of a formal set of rules rather than subjective judgements to detect, adjust and eliminate rogue measurements during the course of an evaluation. It is necessary to specify explicitly the error probability density function (a function specifying the extent to which experimentalists misestimate the uncertainties in their measurements) in order to establish the reliability of a data evaluation procedure, or to compare the effectiveness of different data evaluation procedures. The principles underlying ad hoc and Bayesian data evaluation procedures are compared, and it is concluded that the integrated approach of the Bayesian procedures is to be preferred. An evaluator cannot usually make full use of the information contained in a compilation of measurements (and so minimize the uncertainty in the evaluated value) by considering the measurements in isolation; use must also be made of information gathered from an analysis of other measurements undertaken by the same individuals using the same measuring techniques.
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