Precision and reproducibility are critical for ensuring the reliability of measurement methods, especially in materials testing, where results influence design, safety, and compliance. Interlaboratory studies (round-robin tests) are a central tool in estimating these metrics. Traditionally, ISO 5725-2 has provided the statistical foundation for such analyses, relying on classical ANOVA-based methods and fixed rules for outlier detection. With advances in computational statistics, including robust statistical methods and mixed linear models, the ISO 5725-2:2019 update now explicitly permits modern alternatives. This contribution explores how robust linear mixed-models can enhance the analysis of precision and reproducibility data. The results demonstrate that robust methods provide greater stability against outliers and unbalanced designs, leading to more reliable estimates of variability components. The paper concludes with recommendations for practitioners and standards developers on adopting robust methodologies in precision studies.