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.2024 Jan 8;22(1):e3002423.
doi: 10.1371/journal.pbio.3002423. eCollection 2024 Jan.

Finding the right power balance: Better study design and collaboration can reduce dependence on statistical power

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Finding the right power balance: Better study design and collaboration can reduce dependence on statistical power

Shinichi Nakagawa et al. PLoS Biol..

Abstract

Power analysis currently dominates sample size determination for experiments, particularly in grant and ethics applications. Yet, this focus could paradoxically result in suboptimal study design because publication biases towards studies with the largest effects can lead to the overestimation of effect sizes. In this Essay, we propose a paradigm shift towards better study designs that focus less on statistical power. We also advocate for (pre)registration and obligatory reporting of all results (regardless of statistical significance), better facilitation of team science and multi-institutional collaboration that incorporates heterogenization, and the use of prospective and living meta-analyses to generate generalizable results. Such changes could make science more effective and, potentially, more equitable, helping to cultivate better collaborations.

Copyright: © 2024 Nakagawa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The vicious cycle of power analysis and publication bias.
An example of how effect sizeθ can be inflated via selective publication and how power analysis, in its current use, can encourage this cycle to continue.
Fig 2
Fig 2. Plasticity of a trait in relation to an environmental variable.
Traits are expressed differently (y1 andy2) depending on environmental conditions (x1 and x2). Therefore, excessive standardizations (of environments) will lead to unreplicable results. See Box 3 for the details of differently colored parts.
Fig 3
Fig 3. The virtuous cycle of research.
A visualization of how our proposed paradigm shift could start a virtuous cycle that empowers researchers and better science.
See this image and copyright information in PMC

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