Low statistical power in biomedical science: a review of three human research domains

Current Incentives for Scientists Lead to Underpowered Studies with Erroneous Conclusions

(2016) Andrew D. Higginson et al.   PLOS BIOLOGY

Increasing value and reducing waste in research design, conduct, and analysis

(2014) John P A Ioannidis et al.   LANCET

Article Commentary: On the Persistence of Low Power in Psychological Science

(2014) Ivan Vankov et al.   QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY

Clarifications on the application and interpretation of the test for excess significance and its extensions

(2013) John P.A. Ioannidis   JOURNAL OF MATHEMATICAL PSYCHOLOGY

Empirical evidence for low reproducibility indicates low pre-study odds

(2013) Katherine S. Button et al.   NATURE REVIEWS NEUROSCIENCE

Confidence and precision increase with high statistical power

(2013) Katherine S. Button et al.   NATURE REVIEWS NEUROSCIENCE

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

(2013) Katherine S. Button et al.   NATURE REVIEWS NEUROSCIENCE

Candidate and non-candidate genes in behavior genetics

(2012) Jonathan Flint et al.   CURRENT OPINION IN NEUROBIOLOGY

A peculiar prevalence of p values just below .05

(2012) E.J. Masicampo et al.   QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY

Statistically significant meta-analyses of clinical trials have modest credibility and inflated effects

(2011) Tiago V. Pereira et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Current sample size conventions: Flaws, harms, and alternatives

(2010) Peter Bacchetti   BMC Medicine

Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data

(2009) Byron C Wallace et al.   BMC Medical Research Methodology