The article "Check your outliers! An introduction to identifying statistical outliers in R with easystats" by Rémi Thériault et al. provides an overview of current recommendations and best practices for identifying outliers in statistical data, particularly in the context of R and the *easystats* ecosystem. The authors highlight the importance of robust methods for outlier detection, such as using the median and median absolute deviation (MAD) instead of the mean and standard deviation, which are less robust. They cover univariate, multivariate, and model-based outlier detection methods, detailing their thresholds, standard output, and plotting methods. The article emphasizes the need for transparency in outlier treatment decisions and provides practical examples using the *performance* package in R. The authors also discuss the limitations of traditional methods like $z$-scores and the importance of choosing appropriate methods based on the specific research context. The article concludes by reviewing different types of outliers and the importance of following established guidelines to ensure accurate and reproducible results.The article "Check your outliers! An introduction to identifying statistical outliers in R with easystats" by Rémi Thériault et al. provides an overview of current recommendations and best practices for identifying outliers in statistical data, particularly in the context of R and the *easystats* ecosystem. The authors highlight the importance of robust methods for outlier detection, such as using the median and median absolute deviation (MAD) instead of the mean and standard deviation, which are less robust. They cover univariate, multivariate, and model-based outlier detection methods, detailing their thresholds, standard output, and plotting methods. The article emphasizes the need for transparency in outlier treatment decisions and provides practical examples using the *performance* package in R. The authors also discuss the limitations of traditional methods like $z$-scores and the importance of choosing appropriate methods based on the specific research context. The article concludes by reviewing different types of outliers and the importance of following established guidelines to ensure accurate and reproducible results.