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Description: The Equivalent Score (ES) method is used to score numerous neuropsychological tests. While the ES0 and the ES4 are usually defined respectively by the outer tolerance limit and the median, the intermediate ESs are commonly calculated using a z-score approach even when the distribution of neuropsychological data is typically non-parametric. To calculate more accurate ESs, we propose that the intermediate ESs need to be calculated based on a non-parametric rank subdivision of the distribution of the adjusted scores. With three simulations we investigated the differences between the classical z-score approach, the rank-based approach, and the direct subdivision of the dependent variable. The results show that by subdividing intermediate ESs using the rank-subdivision, neuropsychological tests can be scored more accurately. Thus, future normative data definition should consider the best procedure for scoring with ES. How to cite: Facchin, A., Rizzi, E. & Vezzoli, M. A rank subdivision of equivalent score for enhancing neuropsychological test norms. Neurological Sciences (2022). https://doi.org/10.1007/s10072-022-06140-6