Nature Reviews Neuroscience 14, 365-376 (May 2013) | doi:10.1038/nrn3475
Corrected online: 15 April 2013

There is an Erratum (May 1 2013) associated with this article.

Power failure: why small sample size undermines the reliability of neuroscience

See also: Correspondence by Quilan | Correspondence by Ashton | Correspondence by Bacchetti | Author's reply by Button et al.

Katherine S. Button1,2, John P. A. Ioannidis3, Claire Mokrysz1, Brian A. Nosek4, Jonathan Flint5, Emma S. J. Robinson6 & Marcus R. Munafò1  About the authors


A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.

Author affiliations

  1. School of Experimental Psychology, University of Bristol, Bristol, BS8 1TU, UK.
  2. School of Social and Community Medicine, University of Bristol, Bristol, BS8 2BN, UK.
  3. Stanford University School of Medicine, Stanford, California 94305, USA.
  4. Department of Psychology, University of Virginia, Charlottesville, Virginia 22904, USA.
  5. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK.
  6. School of Physiology and Pharmacology, University of Bristol, Bristol, BS8 1TD, UK.

Correspondence to: Marcus R. Munafò1 Email:

Published online 10 April 2013

* On page 2 of this article, the definition of R should have read: "R is the pre-study odds (that is, the odds that a probed effect is indeed non-null among the effects being probed)". This has been corrected in the online version.