REPROPREP:

Preprocessing strategy selection in business analytics typically relies on convention rather than systematic evidence, despite consuming 60–80% of project effort. This study introduces REPROPREP (v1.0), a methodological framework for validating preprocessing effectiveness assumptions through statistical analysis and cost-benefit assessment. The framework applies Benjamini-Hochberg false discovery rate correction, quality degradation protocols, and cost-effectiveness evaluation. A demonstration across 10 UCI datasets, three preprocessing strategies, and gradient boosting classifiers with 5-fold stratified cross-validation yielded no statistically significant performance differences after multiple comparisons correction (mean effect size: 0.001 AUC), with implementation cost differences ranging from $150–$800. Focused on numeric preprocessing, REPROPREP provides organizations with a rigorous, context-specific methodology for evaluating preprocessing assumptions. Generalizability requires validation beyond tested conditions. Reproducible code is publicly available.