A novel statistical method called ANCOM (Analysis of Composition of Microbiomes) has been developed to analyze microbial composition data. Traditional methods often fail to account for the compositional nature of microbiome data, leading to inflated false discovery rates (FDR) and reduced statistical power. ANCOM addresses these issues by using log-ratio analysis to account for the compositional structure of microbiome data, allowing for more accurate inference about microbial abundance differences between populations. ANCOM is a linear model-based approach that does not assume a specific distribution for the data and can handle thousands of taxa. It was compared to the t-test and Zero Inflated Gaussian (ZIG) methodology in simulations. ANCOM consistently controlled the FDR at the desired level while improving statistical power, whereas the t-test and ZIG showed inflated FDRs, up to 68% for the t-test and 60% for ZIG. ANCOM was applied to two publicly available human gut microbiome datasets, demonstrating its effectiveness in detecting compositional differences. The method was also applied to real data from a study on preterm infants, where it revealed that the abundance of certain bacterial classes was influenced by factors such as delivery mode, antibiotic use, and breast milk, aligning with previous literature. ANCOM was further used to compare gut microbiomes across different age groups and geographical locations, identifying significant differences in microbial composition between populations. ANCOM is computationally efficient and suitable for large datasets, making it a valuable tool for microbiome research. It provides a more accurate and reliable method for analyzing microbial composition data by accounting for the compositional nature of the data, thus improving the validity of biological conclusions.A novel statistical method called ANCOM (Analysis of Composition of Microbiomes) has been developed to analyze microbial composition data. Traditional methods often fail to account for the compositional nature of microbiome data, leading to inflated false discovery rates (FDR) and reduced statistical power. ANCOM addresses these issues by using log-ratio analysis to account for the compositional structure of microbiome data, allowing for more accurate inference about microbial abundance differences between populations. ANCOM is a linear model-based approach that does not assume a specific distribution for the data and can handle thousands of taxa. It was compared to the t-test and Zero Inflated Gaussian (ZIG) methodology in simulations. ANCOM consistently controlled the FDR at the desired level while improving statistical power, whereas the t-test and ZIG showed inflated FDRs, up to 68% for the t-test and 60% for ZIG. ANCOM was applied to two publicly available human gut microbiome datasets, demonstrating its effectiveness in detecting compositional differences. The method was also applied to real data from a study on preterm infants, where it revealed that the abundance of certain bacterial classes was influenced by factors such as delivery mode, antibiotic use, and breast milk, aligning with previous literature. ANCOM was further used to compare gut microbiomes across different age groups and geographical locations, identifying significant differences in microbial composition between populations. ANCOM is computationally efficient and suitable for large datasets, making it a valuable tool for microbiome research. It provides a more accurate and reliable method for analyzing microbial composition data by accounting for the compositional nature of the data, thus improving the validity of biological conclusions.