Accuracy Evaluation of Algorithms for Processing a Posteriori Measurement Results Obtained from Tripled Measurement Channels

The paper considers a problem associated with the evaluation of conditional (refined) confidence limits set for measurement errors using a posteriori information on measurement results. An analysis was carried out to compare the accuracy of various algorithms for processing a posteriori measurement results obtained using three measurement channels of the same type. The algorithms are used to process the results of three equally accurate measurements according to the arithmetic mean and median values, as well as the arithmetic mean of the maximum and minimum values. Here, three parameters serve as a posteriori information: the ratio of the difference between the maximum and minimum results of three measurements to the accuracy indicator of the measurement channel; the ratio of the minimum to the maximum values of two differences (the maximum and median or median and minimum values of the three measurement results); the product of the two above-mentioned parameters. The first and second parameters characterize relative spread and uniformity in the dispersion of results obtained from three measurements, respectively, while the third characterizes density and – as with the second parameter – dispersion uniformity. For these three parameters, boundary values were ascertained, relative to which the conditional confidence limits for the errors of algorithms exceed or fall below the unconditional confidence limits for the errors of these algorithms. In terms of accuracy indicators, a particular algorithm can be rationally applied for processing three equally accurate measurements depending on the error distribution law. Relationships are proposed for evaluating the accuracy indicators of results obtained when processing these measurement data.