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Machine Learning Can Solve the Reproducibility Crisis by Supplanting Reductionist Statistics

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  • Sadri, Arash

Abstract

Resolving the “replication crisis” is a top priority of the scientific community now. Numerous proposals have been made; still, there lacks not only an established solution but even an agreement on whether there exists a “crisis” or not. Here, by questioning the philosophical foundations of our study designs and analyses, I trace back the “crisis” to reductionist ontologies and methodologies ingrained in the modern statistical methods which have dominated biological, medical, psychological, and social sciences for a century. The crisis is not our inability to “reproduce” results but that, uncritical of our statistical methods and the experimental designs they have inculcated, we expect to be able to “reproduce” results despite neglecting almost all individual-level and contextual variables of complex processes.

Suggested Citation

  • Sadri, Arash, 2022. "Machine Learning Can Solve the Reproducibility Crisis by Supplanting Reductionist Statistics," MetaArXiv yxba5_v1, Center for Open Science.
    • Handle: RePEc:osf:metaar:yxba5_v1
    DOI: 10.31219/osf.io/yxba5_v1
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