The Quantitative Psychologist Is Dead, Long Live the Quantitative Psychologist
Abstract
Much of what quantitative psychologists publish consists of developing statistical methods, implementing them in software, and evaluating them in simulation studies. I argue that artificial intelligence (AI) can already do much of this work, and that its share will only grow. The quantitative psychologist has always been a jack of all trades, combining programming, statistics, and psychological knowledge without being a master of any one of them. AI is now the better jack of all trades. When methods become cheap, value moves to what AI cannot supply and to what it newly makes possible. I describe three directions: collecting trustworthy data from real people, formalizing psychological theory now that computational modeling is within reach of any researcher, and rethinking methodological education so that students learn to judge the analyses that AI will run for them. The specialist role built around producing methods for others will fade, while quantitative thinking spreads through all of psychology.
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BibTeX
@misc{epskamp2026quantitative,
title = {{The Quantitative Psychologist Is Dead, Long Live the Quantitative Psychologist}},
author = {Sacha Epskamp},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/8yxwb_v1},
url = {https://osf.io/preprints/psyarxiv/8yxwb_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Quantitative Psychologist Is Dead, Long Live the Quantitative Psychologist
AU - Sacha Epskamp
PY - 2026
DA - 2026/10/25
PB - PsyArXiv
DO - 10.31234/osf.io/8yxwb_v1
UR - https://osf.io/preprints/psyarxiv/8yxwb_v1/
AB - Much of what quantitative psychologists publish consists of developing statistical methods, implementing them in software, and evaluating them in simulation studies. I argue that artificial intelligence (AI) can already do much of this work, and that its share will only grow. The quantitative psychologist has always been a jack of all trades, combining programming, statistics, and psychological knowledge without being a master of any one of them. AI is now the better jack of all trades. When methods become cheap, value moves to what AI cannot supply and to what it newly makes possible. I describe three directions: collecting trustworthy data from real people, formalizing psychological theory now that computational modeling is within reach of any researcher, and rethinking methodological education so that students learn to judge the analyses that AI will run for them. The specialist role built around producing methods for others will fade, while quantitative thinking spreads through all of psychology.
ER -