%0 DATA
%A Estelle, Dumas-Mallet
%A Katherine, S. Button
%A Thomas, Boraud
%A Francois, Gonon
%A Marcus, R. Munafò
%D 2017
%T Figures S1-S12. Flow charts for inclusion of studies from Low statistical power in biomedical science: a review of three human research domains
%U https://rs.figshare.com/articles/journal_contribution/Figures_S1-S12_Flow_charts_for_inclusion_of_studies_from_Low_statistical_power_in_biomedical_science_a_review_of_three_human_research_domains/4564699
%R 10.6084/m9.figshare.4564699.v1
%2 https://ndownloader.figshare.com/files/7395316
%K statistical power
%K reproducibility
%K neurology
%K psychiatry
%K somatic disease
%X Studies with low statistical power increase the likelihood that a statistically significant finding represents a false positive result. We conducted a review of meta-analyses of studies investigating the association of biological, environmental or cognitive parameters with neurological, psychiatric and somatic diseases, excluding treatment studies, in order to estimate the average statistical power across these domains. Taking the effect size indicated by a meta-analysis as the best estimate of the likely true effect size, and assuming a threshold for declaring statistical significance of 5%, we found that approximately 50% of studies have statistical power in the 0–10% or 11–20% range, well below the minimum of 80% that is often considered conventional. Studies with low statistical power appear to be common in the biomedical sciences, at least in the specific subject areas captured by our search strategy. However, we also observe evidence that this depends in part on research methodology, with candidate gene studies showing very low average power and studies using cognitive/behavioural measures showing high average power. This warrants further investigation.