A major challenge in the analysis and interpretation of amplicon sequencing data remains the relative nature of the data, which may not reflect actual microbial abundances (see section 4). To circumvent challenges associated with compositional data, numerous tools and normalization methods have been introduced to improve statistical analysis. We here suggest such data-driven approaches to address the topics of normalization, false-discovery rates and the compositional nature of sequencing.