Outlier management in data analysis: a checklist for authors and reviewers

The improper handling of outliers in the analysis of variance (ANOVA) presents a persistent challenge in forestry research, which may lead to biased results, inflated Type I error rates, and obscured scientific signals. The current practice is often an ad hoc method, potentially driven by a need to achieve statistical significance rather than principled scientific reasoning. This Editorial paper addresses this systemic issue by proposing a structured, step-by-step framework for the diagnosis and management of outliers. The framework guides researchers to first investigate the cause of an outlier (data error, measurement error, or genuine extreme value), then statistically assess its impact on ANOVA results and assumptions, and finally, make a transparent decision on its treatment. We strongly advise against the statistically problematic practice of replacing outliers with the mean of other replicates, as it violates data integrity and obscures true variability. Instead, we recommend robust alternatives, including data transformation, non-parametric tests, or the use of trimmed means. This approach aims to uphold statistical robustness and scientific integrity, thereby improving the rigor of forestry research and its publications.