ArticlePloS one2019
P-curve won't do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016).
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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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Who cites it
4 citing papers in PubMed.
- Theta beta ratio in attention deficit hyperactivity disorder using a multiverse analysis.eLife · 2026Article
- Periodontitis bone loss detection in panoramic radiographs using modified YOLOv7.PeerJ. Computer science · 2025Article
- Reproducible research into human chemical communication by cues and pheromones: learning from psychology's renaissance.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Review
- Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.NMR in biomedicine · 2020Article
Corrections and comments
- Comment on
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
p-curve, the distribution of significant p-values, can be analyzed to assess if the findings have evidential value, whether p-hacking and file-drawering can be ruled out as the sole explanations for them. Bruns and Ioannidis (2016) have proposed p-curve cannot examine evidential value with observational data. Their discussion confuses false-positive findings with confounded ones, failing to distinguish correlation from causation. We demonstrate this important distinction by showing that a confounded but real, hence replicable association, gun ownership and number of sexual partners, leads to a right-skewed p-curve, while a false-positive one, respondent ID number and trust in the supreme court, leads to a flat p-curve. P-curve can distinguish between replicable and non-replicable findings. The observational nature of the data is not consequential.
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Registered trials
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