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

Author:

Nakagawa ShinichiORCID,Lagisz MalgorzataORCID,Yang YefengORCID,Drobniak Szymon M.ORCID

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.

Funder

Australian Research Council

Publisher

Public Library of Science (PLoS)

Subject

General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Neuroscience

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