Bootstrap-based estimation and inference for measurement precision under ISO 5725

TL;DR

This paper develops bootstrap resampling methods for estimating measurement precision variances in ISO 5725 interlaboratory studies, providing practical guidance and comparing with traditional ANOVA approaches through simulations and a case study.

Contribution

It introduces tailored bootstrap strategies for variance component estimation in ISO 5725, including bias correction and two-stage resampling, with extensive evaluation and practical recommendations.

Findings

Adjusted within-laboratory resampling yields accurate point estimates.

Two-stage resampling with bias-corrected intervals provides reliable confidence intervals.

Performance declines with very small samples or high between-laboratory variation.

Abstract

The ISO 5725 series frames interlaboratory precision through repeatability, between-laboratory, and reproducibility variances, yet practical guidance on deploying bootstrap methods within this one-way random-effects setting remains limited. We study resampling strategies tailored to ISO 5725 data and extend a bias-correction idea to obtain simple adjusted point estimators and confidence intervals for the variance components. Using extensive simulations that mirror realistic study sizes and variance ratios, we evaluate accuracy, stability, and coverage, and we contrast the resampling-based procedures with ANOVA-based estimators and common approximate intervals. The results yield a clear division of labor: adjusted within-laboratory resampling provides accurate and stable point estimation in small-to-moderate designs, whereas a two-stage strategy-resampling laboratories and then resampling within each-paired with bias-corrected and accelerated intervals offers the most reliable (near-nominal or conservative) confidence intervals. Performance degrades under extreme designs, such as very small samples or dominant between-laboratory variation, clarifying when additional caution is warranted. A case study from an ISO 5725-4 dataset illustrates how the recommended procedures behave in practice and how they compare with ANOVA and approximate methods. We conclude with concrete guidance for implementing resampling-based precision analysis in interlaboratory studies: use adjusted within-laboratory resampling for point estimation, and adopt the two-stage strategy with bias-corrected and accelerated intervals for interval estimation.