UniFrac is a β-diversity measure that uses phylogenetic information to compare microbial communities. It has been widely applied in over 150 research publications to understand microbial community relationships in various systems, from human disease to general ecology. UniFrac measures the evolutionary history unique to each sample, providing insights into microbial community differences. However, a recent simulation study by Schloss (2008) concluded that UniFrac is unsuitable as a distance metric for multivariate analysis due to its sensitivity to sampling depth and lack of consistent linear correlation with community overlap. In response, the authors of this commentary reassess the data and provide a mathematical proof showing that UniFrac meets the formal requirements of a distance metric, including non-negativity, symmetry, and the triangle inequality. They confirm that UniFrac values can indeed be influenced by the number of sequences per sample, particularly in smaller samples, and recommend sequence jackknifing to address this issue. The authors argue that the sensitivity to sampling depth does not make UniFrac unsuitable but rather highlights the need for standardizing the number of sequences per sample or using jackknifing techniques. They emphasize that pairing UniFrac with multivariate statistical methods remains a powerful approach for analyzing complex microbial community data, despite potential shortcomings in undersampled environments.UniFrac is a β-diversity measure that uses phylogenetic information to compare microbial communities. It has been widely applied in over 150 research publications to understand microbial community relationships in various systems, from human disease to general ecology. UniFrac measures the evolutionary history unique to each sample, providing insights into microbial community differences. However, a recent simulation study by Schloss (2008) concluded that UniFrac is unsuitable as a distance metric for multivariate analysis due to its sensitivity to sampling depth and lack of consistent linear correlation with community overlap. In response, the authors of this commentary reassess the data and provide a mathematical proof showing that UniFrac meets the formal requirements of a distance metric, including non-negativity, symmetry, and the triangle inequality. They confirm that UniFrac values can indeed be influenced by the number of sequences per sample, particularly in smaller samples, and recommend sequence jackknifing to address this issue. The authors argue that the sensitivity to sampling depth does not make UniFrac unsuitable but rather highlights the need for standardizing the number of sequences per sample or using jackknifing techniques. They emphasize that pairing UniFrac with multivariate statistical methods remains a powerful approach for analyzing complex microbial community data, despite potential shortcomings in undersampled environments.