ArticleNeuroImage2013
Why small low-powered studies are worse than large high-powered studies and how to protect against "trivial" findings in research: comment on Friston (2012).
Article in NeuroImage, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Efficacy and safety of yoga for the management of chronic low back pain: an overview of systematic reviews.Frontiers in neurology · 2023Pooled it
- Visual Task Demands and the Auditory Mismatch Negativity: An Empirical Study and a Meta-Analysis.PloS one · 2016Pooled it
- Potential reporting bias in fMRI studies of the brain.PloS one · 2013Pooled it
- Bias in data-driven replicability analysis of univariate brain-wide association studies.Scientific reports · 2025Article
- Oral language interventions can improve language outcomes in children with neurodevelopmental disorders: A systematic review and meta-analysis.Campbell systematic reviews · 2023Review
- Evaluation of a decided sample size in machine learning applications.BMC bioinformatics · 2023Article
- Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders.Neuropsychology review · 2022Review
- The effects of intranasal oxytocin on black participants' responses to outgroup acceptance and rejection.Frontiers in psychology · 2022Article
- Detecting Suicide Ideation in the Era of Social Media: The Population Neuroscience Perspective.Frontiers in psychiatry · 2022Article
- Influence of sample size and analytic approach on stability and interpretation of brain-behavior correlations in task-related fMRI data.Human brain mapping · 2021Article
- Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies.Frontiers in neuroinformatics · 2020Article
- Discovery reliability.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2019Article
- Sleep restriction caused impaired emotional regulation without detectable brain activation changes-a functional magnetic resonance imaging study.Royal Society open science · 2019Article
- Visual Perceptual Load Does Not Affect the Frequency Mismatch Negativity.Frontiers in psychology · 2019Article
- Sparse Sampling of Silence Type I Errors With an Emphasis on Primary Auditory Cortex.Frontiers in neuroscience · 2019Article
- Estimating statistical power, posterior probability and publication bias of psychological research using the observed replication rate.Royal Society open science · 2018Article
- The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings.Neuropsychologia · 2018Article
- The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences.Behavior research methods · 2018Article
- Small sample sizes reduce the replicability of task-based fMRI studies.Communications biology · 2018Article
- A Systematic Review and Meta-Analysis of the Relationship Between Brain Data and the Outcome in Disorders of Consciousness.Frontiers in neurology · 2018Article
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Abstract
It is sometimes argued that small studies provide better evidence for reported effects because they are less likely to report findings with small and trivial effect sizes (Friston, 2012). But larger studies are actually better at protecting against inferences from trivial effect sizes, if researchers just make use of effect sizes and confidence intervals. Poor statistical power also comes at a cost of inflated proportion of false positive findings, less power to "confirm" true effects and bias in reported (inflated) effect sizes. Small studies (n=16) lack the precision to reliably distinguish small and medium to large effect sizes (r<.50) from random noise (α=.05) that larger studies (n=100) does with high level of confidence (r=.50, p=.00000012). The present paper presents the arguments needed for researchers to refute the claim that small low-powered studies have a higher degree of scientific evidence than large high-powered studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.