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.

翻译：ISO 5725系列标准通过重复性方差、实验室间方差和再现性方差来构建实验室间精度框架，然而在此单因素随机效应模型下应用Bootstrap方法的实际指导仍然有限。我们研究了针对ISO 5725数据定制的重抽样策略，并扩展了一种偏差校正思想，以获得方差分量的简单调整点估计量与置信区间。通过模拟反映实际研究规模和方差比的广泛仿真，我们评估了准确性、稳定性和覆盖率，并将基于重抽样的程序与基于方差分析（ANOVA）的估计量及常见近似区间进行了对比。结果呈现出明确的分工：调整后的实验室内重抽样在中小规模设计中提供准确稳定的点估计，而两阶段策略——先对实验室进行重抽样，再在每个实验室内进行重抽样——结合偏差校正与加速（BCa）区间，则能提供最可靠（接近名义水平或保守）的置信区间。在极端设计下（如样本量极小或实验室间变异占主导地位），性能会下降，这明确了何时需要额外谨慎。基于ISO 5725-4数据集的案例研究说明了推荐程序在实际中的表现，以及它们与ANOVA和近似方法的比较。最后，我们为在实验室间研究中实施基于重抽样的精度分析提供了具体指导：使用调整后的实验室内重抽样进行点估计，并采用结合偏差校正与加速区间的两阶段策略进行区间估计。