When Tukey meets Chauvenet: a new boxplot criterion for outlier detection

TL;DR

This paper introduces a new outlier detection method called the Chauvenet-type boxplot, combining Tukey's boxplot and Chauvenet's criterion, which is robust, easy to implement, and effective across different sample sizes.

Contribution

The paper proposes a novel outlier detection criterion that integrates Tukey's boxplot with Chauvenet's rule, providing a robust and practical alternative for outlier identification.

Findings

Performs well regardless of sample size

Maintains simplicity and robustness in outlier detection

Outperforms traditional methods in simulations and real data

Abstract

The box-and-whisker plot, introduced by Tukey (1977), is one of the most popular graphical methods in descriptive statistics. On the other hand, however, Tukey's boxplot is free of sample size, yielding the so-called "one-size-fits-all" fences for outlier detection. Although improvements on the sample size adjusted boxplots do exist in the literature, most of them are either not easy to implement or lack justification. As another common rule for outlier detection, Chauvenet's criterion uses the sample mean and standard derivation to perform the test, but it is often sensitive to the included outliers and hence is not robust. In this paper, by combining Tukey's boxplot and Chauvenet's criterion, we introduce a new boxplot, namely the Chauvenet-type boxplot, with the fence coefficient determined by an exact control of the outside rate per observation. Our new outlier criterion not only maintains the simplicity of the boxplot from a practical perspective, but also serves as a robust Chauvenet's criterion. Simulation study and a real data analysis on the civil service pay adjustment in Hong Kong demonstrate that the Chauvenet-type boxplot performs extremely well regardless of the sample size, and can therefore be highly recommended for practical use to replace both Tukey's boxplot and Chauvenet's criterion. Lastly, to increase the visibility of the work, a user-friendly R package named `ChauBoxplot' has also been officially released on CRAN.