This study introduces a novel and universal method for normalizing microRNA (miRNA) real-time quantitative PCR (RT-qPCR) data using the mean expression value of all expressed miRNAs in a given sample. The authors compared this method to the current approach, which uses small nuclear/nucleolar RNAs as reference genes. They found that the mean expression value outperforms the current normalization strategy in terms of reducing technical variation and accurately representing biological changes. The study evaluated the stability of the mean miRNA expression value using the geNorm algorithm and demonstrated its effectiveness in reducing technical variation. Additionally, the mean expression value was shown to identify true biological changes in patient samples and cell lines, particularly in the context of MYCN-regulated miRNA clusters. The authors also proposed a workflow for selecting miRNAs or small RNA controls that resemble the mean expression value for small-scale experiments. Overall, the mean expression value normalization method is innovative, straightforward, and universally applicable, enabling a more accurate assessment of relevant biological variation from miRNA RT-qPCR experiments.This study introduces a novel and universal method for normalizing microRNA (miRNA) real-time quantitative PCR (RT-qPCR) data using the mean expression value of all expressed miRNAs in a given sample. The authors compared this method to the current approach, which uses small nuclear/nucleolar RNAs as reference genes. They found that the mean expression value outperforms the current normalization strategy in terms of reducing technical variation and accurately representing biological changes. The study evaluated the stability of the mean miRNA expression value using the geNorm algorithm and demonstrated its effectiveness in reducing technical variation. Additionally, the mean expression value was shown to identify true biological changes in patient samples and cell lines, particularly in the context of MYCN-regulated miRNA clusters. The authors also proposed a workflow for selecting miRNAs or small RNA controls that resemble the mean expression value for small-scale experiments. Overall, the mean expression value normalization method is innovative, straightforward, and universally applicable, enabling a more accurate assessment of relevant biological variation from miRNA RT-qPCR experiments.