Our study introduced a novel approach to machine learning, where decision-making stages were made based on statistical tests. We utilized 16 different molecular fingerprints and descriptors, and employed the Wilcoxon signed-rank test of cross-validation to determine the optimal one for feature and model selection. The LOF algorithm was implemented to establish the applicability domain, outperforming the bounding box technique in sparse areas.