Severn, Daniel ErnestSteiner, Stefan H.MacKay, R. Jock2020-01-052020-01-052019-11https://doi.org/10.1016/j.measurement.2019.06.019http://hdl.handle.net/10012/15394The final publication is available at Elsevier via https://doi.org/10.1016/j.measurement.2019.06.019. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/We investigate efficient plans to assess the misclassification error rates of a binary measurement system used as an in-line inspection protocol. We assume that parts can be inspected repeatedly and that each part has its own (latent) misclassification rate. We propose a three-phase assessment plan. Phase I consists of data from recent inspection history. In Phase II, we select a sample of failed parts that we re-measure multiple times with the binary measurement system of interest. In Phase III, we verify a carefully selected subsample of the parts from Phase II with the aid of a binary gold standard measurement system. We show that the proposed plan is a substantial improvement over existing assessment plans in terms of cost and/or precision.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/baseline databinary measurement system assessmentconditional samplingtargeted verificationArticle