Are Small Effects the Indispensable Foundation for a Cumulative Psychological Science? A Reply to Götz et al. (2022)

  • Maximilian A. Primbs (Corresponding author)
  • , Charlotte R. Pennington
  • , Daniël Lakens
  • , Miguel Alejandro A. Silan
  • , Dwayne S.N. Lieck
  • , Patrick S. Forscher
  • , Erin M. Buchanan
  • , Samuel J. Westwood

Research output: Contribution to journalArticleAcademicpeer-review

48 Citations (Scopus)
138 Downloads (Pure)

Abstract

In the January 2022 issue of Perspectives, Götz et al. argued that small effects are “the indispensable foundation for a cumulative psychological science.” They supported their argument by claiming that (a) psychology, like genetics, consists of complex phenomena explained by additive small effects; (b) psychological-research culture rewards large effects, which means small effects are being ignored; and (c) small effects become meaningful at scale and over time. We rebut these claims with three objections: First, the analogy between genetics and psychology is misleading; second, p values are the main currency for publication in psychology, meaning that any biases in the literature are (currently) caused by pressure to publish statistically significant results and not large effects; and third, claims regarding small effects as important and consequential must be supported by empirical evidence or, at least, a falsifiable line of reasoning. If accepted uncritically, we believe the arguments of Götz et al. could be used as a blanket justification for the importance of any and all “small” effects, thereby undermining best practices in effect-size interpretation. We end with guidance on evaluating effect sizes in relative, not absolute, terms.

Original languageEnglish
Pages (from-to)508-512
Number of pages5
JournalPerspectives on Psychological Science
Volume18
Issue number2
DOIs
Publication statusPublished - Mar 2023

Bibliographical note

Publisher Copyright:
© The Author(s) 2022.

Funding

We thank Stuart J. Ritchie, Jonathan Coleman, and Freek Oude Maatman for their comments on earlier drafts of this article.

Keywords

  • benchmarks
  • effect sizes
  • practical significance
  • small effects
  • statistical inference
  • Humans
  • Reward

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