The article by Donovan H. Parks and Robert G. Beiko discusses the challenges and methods for identifying biologically relevant differences between metagenomic communities. Metagenomics, the study of genetic material from environmental samples, has revealed a wealth of microbial diversity and complexity. However, distinguishing ecological influences from sampling artifacts is crucial for making meaningful biological inferences. The authors introduce STAMP (STatistical Analysis of Metagenomic Profiles), a new software package designed to support best practices in comparative metagenomics analysis and reporting. STAMP provides a user-friendly graphical environment for performing statistical techniques, including Fisher’s exact test, chi-square and G-tests, and bootstrapping. The software also supports effect size statistics, confidence intervals, and multiple hypothesis test correction methods. The article includes a detailed implementation guide and a comparison with existing software tools. Two case studies are presented: one comparing metagenomes from iron mines and another analyzing functional profiles of 'Candidatus Accumulibacter phosphatis' strains in enhanced biological phosphorus removal metagenomes. These examples demonstrate how STAMP can help identify biologically significant subsystems and highlight the importance of considering effect sizes and confidence intervals in interpreting statistical results.The article by Donovan H. Parks and Robert G. Beiko discusses the challenges and methods for identifying biologically relevant differences between metagenomic communities. Metagenomics, the study of genetic material from environmental samples, has revealed a wealth of microbial diversity and complexity. However, distinguishing ecological influences from sampling artifacts is crucial for making meaningful biological inferences. The authors introduce STAMP (STatistical Analysis of Metagenomic Profiles), a new software package designed to support best practices in comparative metagenomics analysis and reporting. STAMP provides a user-friendly graphical environment for performing statistical techniques, including Fisher’s exact test, chi-square and G-tests, and bootstrapping. The software also supports effect size statistics, confidence intervals, and multiple hypothesis test correction methods. The article includes a detailed implementation guide and a comparison with existing software tools. Two case studies are presented: one comparing metagenomes from iron mines and another analyzing functional profiles of 'Candidatus Accumulibacter phosphatis' strains in enhanced biological phosphorus removal metagenomes. These examples demonstrate how STAMP can help identify biologically significant subsystems and highlight the importance of considering effect sizes and confidence intervals in interpreting statistical results.