Tukey's boxplot is widely used for outlier detection; however, its classic fixed-fence rule tends to flag an excessive number of outliers as the sample size grows. To address this, we introduce two new R packages, ChauBoxplot and AdaptiveBoxplot, which implement more robust and statistically principled outlier detection methods. We illustrate their advantages and practical implications through comprehensive simulation studies and a real-world analysis of provincial university admission rates from China's National College Entrance Examination. Based on these findings, we provide practical guidance to help practitioners select appropriate boxplot methods, achieving a balance between interpretability and statistical reliability.

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To cite this article: Tiejun Tong, Hongmei Lin, Bowen Gang & Riquan Zhang (24 Mar 2026): ChauBoxplot and AdaptiveBoxplot: two R packages for boxplot-based outlier detection, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2026.2642439

To link to this article: https://doi.org/10.1080/24754269.2026.2642439