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 language | English |
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Pages (from-to) | 174569162211004 |
Number of pages | 1 |
Journal | Perspectives on Psychological Science |
Early online date | 20 Sept 2022 |
DOIs | |
Publication status | E-pub ahead of print - 20 Sept 2022 |
Bibliographical note
© The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).Funding Information:
FundingThis work was funded by the Netherlands Organisation for Scientific Research VIDI Grant 452-17-013 (to D. Lakens).
Keywords
- effect sizes
- small effects
- benchmarks
- practical significance
- statistical inference