A novel and universal method for microRNA RT-qPCR data normalization has been proposed. The study demonstrates that using the mean expression value of all expressed microRNAs in a sample as a normalization factor outperforms the current approach based on small nuclear/nucleolar RNAs. This method provides better reduction of technical variation and more accurate appreciation of biological changes. The mean expression value was evaluated in various sample sets, including neuroblastoma (NB) and T-cell acute lymphoblastic leukemia (T-ALL) samples, and was found to be highly stable and effective for normalization. It outperformed previously proposed universal reference miRNAs in terms of expression stability. The method was also shown to reduce technical variation more effectively than normalization using stable small RNA controls. The study further shows that the mean expression value normalization identifies true biological changes in patient samples and cell lines, and reduces false positive results. It was also found to be platform-independent and effective for both large-scale and small-scale miRNA profiling. The method was validated across different datasets and was shown to be more accurate than current normalization strategies. The study concludes that the mean expression value normalization is a robust and universally applicable method for miRNA RT-qPCR data normalization.A novel and universal method for microRNA RT-qPCR data normalization has been proposed. The study demonstrates that using the mean expression value of all expressed microRNAs in a sample as a normalization factor outperforms the current approach based on small nuclear/nucleolar RNAs. This method provides better reduction of technical variation and more accurate appreciation of biological changes. The mean expression value was evaluated in various sample sets, including neuroblastoma (NB) and T-cell acute lymphoblastic leukemia (T-ALL) samples, and was found to be highly stable and effective for normalization. It outperformed previously proposed universal reference miRNAs in terms of expression stability. The method was also shown to reduce technical variation more effectively than normalization using stable small RNA controls. The study further shows that the mean expression value normalization identifies true biological changes in patient samples and cell lines, and reduces false positive results. It was also found to be platform-independent and effective for both large-scale and small-scale miRNA profiling. The method was validated across different datasets and was shown to be more accurate than current normalization strategies. The study concludes that the mean expression value normalization is a robust and universally applicable method for miRNA RT-qPCR data normalization.