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

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  • Michael Brimacombe

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.

Suggested Citation

  • Michael Brimacombe, 2025. "Simpson’s Paradox: Aggregation Effects in Statistical and Machine Learning Models," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 14(2), pages 1-5, July.
    • Handle: RePEc:ibn:ijspjl:v:14:y:2025:i:2:p:5
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    References listed on IDEAS

    as
    1. Su-In Lee & Safiye Celik & Benjamin A. Logsdon & Scott M. Lundberg & Timothy J. Martins & Vivian G. Oehler & Elihu H. Estey & Chris P. Miller & Sylvia Chien & Jin Dai & Akanksha Saxena & C. Anthony Bl, 2018. "A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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