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Evaluating Inter-Laboratory Comparison Data



Enrico Frahm, John D. Wright


The primary purpose of inter-laboratory comparisons is to demonstrate that the uncertainty specifications of the calibration measurement capabilities of the participating laboratories are correct. The most common criterion for assessing a participating laboratory's results is whether the normalized error |〖En〗_i | is ≤ 1. Most comparison reports we reviewed properly include uncertainty components related to the transfer standard 〖(u〗_TS) and the repeatability of the calibrations 〖(u〗_(repeat_i )) in the uncertainty of the value reported by a participant. Unfortunately, high values for either u_TS and u_(repeat_i ) decrease |〖En〗_i |, making it easier to achieve passing results in a comparison that uses a poor transfer standard or for a participant that delivers unstable measurements. A review of past comparison reports shows that this problem occurs for many measurands, including flow, temperature, and pressure. Improved comparison criteria were proposed by [1] to counteract the flaws of the |〖En〗_i |≤ 1 criterion by introducing the possibility of inconclusive results and a probability-based approach. In this paper, we define comparison uncertainty u_comp as the root-sum-of-squares of u_TS and u_(repeat_i ) and find it a better tool for assessing the power of the comparison than u_TS alone. We applied the comparison evaluation criteria to recent comparison results to illustrate their benefits over the |〖En〗_i |≤ 1criterion. In general, the newer criteria confirm prior determinations, but in some cases passing results for the |〖En〗_i |≤ 1criterion would be found inconclusive.
Proceedings Title
Conference Dates
October 17-21, 2022
Conference Location
Chongqing, CN
Conference Title


Inter-laboratory comparison, normalized error, inconclusive, probability based criterion


Frahm, E. and Wright, J. (2022), Evaluating Inter-Laboratory Comparison Data, FLOMEKO 2022, Chongqing, CN, [online], (Accessed May 23, 2024)


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Created November 3, 2022, Updated December 2, 2022