Label-free quantification is a powerful method for studying cellular protein phosphorylation dynamics. However, whether current data normalization methods achieve sufficient accuracy has not been examined systematically. Here, we demonstrate that a large uni-directional shift in the phosphopeptide abundance distribution is problematic for global median centering and quantile-based normalizations and may mislead the biological conclusion from unlabeled phosphoproteome data. Instead, we present a novel normalization strategy, named pairwise normalization, which is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of pairwise normalization was validated by statistical methods, western blotting analysis, and by bioinformatics analysis. In addition, we demonstrate that the choice of normalization method influences the downstream analyses of the data and perceived pathway activities. Furthermore, we demonstrate that the developed normalization method, combined with pathway analysis algorithms, revealed a novel biological synergism between Ras signalling and PP2A inhibition by CIP2A.