Simpson’s Paradox: Aggregation Effects in Statistical and Machine Learning Models

Michael Brimacombe

International Journal of Statistics and Probability, 2025, vol. 14, issue 2, 5

Abstract: Data aggregation effects are examined in relation to both statistical and machine learning approaches to data modeling. It is shown that heavily data-centric artificial neural network and random forest methods are subject to aggregation effects similar to those affecting statistical methods. Several basic examples are discussed.

Date: 2025
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