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)
Reference104 articles.
1. Avoidable waste in the production and reporting of research evidence;I Chalmers;Lancet,2009
2. Quantifying research waste in ecology;M Purgar;Nat Ecol Evol,2022
3. Why most published research findings are false.;JPA Ioannidis;PLoS Med.,2005
4. Scientists rise up against statistical significance;V Amrhein;Nature,2019
5. Moving to a World Beyond "p < 0.05".;RL Wasserstein;Am Stat,2019
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