Normalization is crucial because it allows researchers to make meaningful comparisons between samples. Without proper normalization, data from different experiments or platforms could be misleading. For instance, in gene expression studies, normalization corrects for differences in RNA quantity and quality, ensuring that observed changes in gene expression are genuine and biologically relevant.
