ArticlePeerJ2016
Problems in using p-curve analysis and text-mining to detect rate of p-hacking and evidential value.
Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 6 of them syntheses that pooled it.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Pooled it
- A systematic review and meta-analysis of the impact of cash transfers on subjective well-being and mental health in low- and middle-income countries.Nature human behaviour · 2022Pooled it
- Publication and related biases in health services research: a systematic review of empirical evidence.BMC medical research methodology · 2020Pooled it
- What is the effect of active ingredients in dentifrice on inhibiting the regrowth of overnight plaque? A systematic review.International journal of dental hygiene · 2020Pooled it
- Methodological Challenges to the Evaluation of Interventions for Foster/Kinship Carers and Children: A Systematic Review.Clinical child and family psychology review · 2018Pooled it
- Working memory training revisited: A multi-level meta-analysis of n-back training studies.Psychonomic bulletin & review · 2017Pooled it
- Analysis of indications for selectively missing results in comparative registry-based studies in medicine: a meta-research study.Research integrity and peer review · 2025Article
- Is There Evidence of P-Hacking in Imaging Research?Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes · 2023Article
- Big little lies: a compendium and simulation ofRoyal Society open science · 2023Article
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- 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
- Moving Sport and Exercise Science Forward: A Call for the Adoption of More Transparent Research Practices.Sports medicine (Auckland, N.Z.) · 2020Article
- Statistical Significance Filtering Overestimates Effects and Impedes Falsification: A Critique of.Frontiers in psychology · 2020Article
- P-Hacking Lexical Richness Through Definitions of "Type" and "Token".Studies in health technology and informatics · 2019Article
- Open science challenges, benefits and tips in early career and beyond.PLoS biology · 2019Article
- Evidence that nonsignificant results are sometimes preferred: Reverse P-hacking or selective reporting?PLoS biology · 2019Article
- Opinion: Is science really facing a reproducibility crisis, and do we need it to?Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- The earth is flat (PeerJ · 2017Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Background. The p-curve is a plot of the distribution of p-values reported in a set of scientific studies. Comparisons between ranges of p-values have been used to evaluate fields of research in terms of the extent to which studies have genuine evidential value, and the extent to which they suffer from bias in the selection of variables and analyses for publication, p-hacking. Methods. p-hacking can take various forms. Here we used R code to simulate the use of ghost variables, where an experimenter gathers data on several dependent variables but reports only those with statistically significant effects. We also examined a text-mined dataset used by Head et al. (2015) and assessed its suitability for investigating p-hacking. Results. We show that when there is ghost p-hacking, the shape of the p-curve depends on whether dependent variables are intercorrelated. For uncorrelated variables, simulated p-hacked data do not give the "p-hacking bump" just below .05 that is regarded as evidence of p-hacking, though there is a negative skew when simulated variables are inter-correlated. The way p-curves vary according to features of underlying data poses problems when automated text mining is used to detect p-values in heterogeneous sets of published papers. Conclusions. The absence of a bump in the p-curve is not indicative of lack of p-hacking. Furthermore, while studies with evidential value will usually generate a right-skewed p-curve, we cannot treat a right-skewed p-curve as an indicator of the extent of evidential value, unless we have a model specific to the type of p-values entered into the analysis. We conclude that it is not feasible to use the p-curve to estimate the extent of p-hacking and evidential value unless there is considerable control over the type of data entered into the analysis. In particular, p-hacking with ghost variables is likely to be missed.
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What OpenQuestion holds
Registered trials
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.