ReviewNature human behaviour2021
Seven steps toward more transparency in statistical practice.
Review in Nature human behaviour, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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.
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.
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Who cites it
22 citing papers in PubMed, 59 citations in OpenAlex.
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- A multi-disciplinary exploration of the press review of the Italian National Institute of Health during the COVID-19 pandemic.Human vaccines & immunotherapeutics · 2025Article
- Improving statistical reporting in psychology.Communications psychology · 2025Review
- A practice-oriented guide to statistical inference in linear modeling for non-normal or heteroskedastic error distributions.Behavior research methods · 2025Article
- Experimenter evidence unmasking as a confound in optional stopping.Behavior research methods · 2025Article
- Estimating the extent and sources of model uncertainty in political science.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Marital status and risk of cardiovascular disease - a multi-analyst study in epidemiology.European journal of epidemiology · 2025Article
- The temporal trajectories of habit decay in daily life: An intensive longitudinal study on four health-risk behaviors.Applied psychology. Health and well-being · 2025Article
- A manifesto for a globally diverse, equitable, and inclusive open science.Communications psychology · 2025Review
- Refining Bayesian hierarchical MPT modeling: Integrating prior knowledge and ordinal expectations.Behavior research methods · 2024Article
- Ten simple rules for successfully carrying out funded research projects.PLoS computational biology · 2024Article
- Prevalence, patterns and predictors of paranormal beliefs in The Netherlands: a several-analysts approach.Royal Society open science · 2024Article
- Code sharing in ecology and evolution increases citation rates but remains uncommon.Ecology and evolution · 2024Article
- Opening the Black Box of Family-Based Treatments: An Artificial Intelligence Framework to Examine Therapeutic Alliance and Therapist Empathy.Clinical child and family psychology review · 2023Article
- Multibridge: an R package to evaluate informed hypotheses in binomial and multinomial models.Behavior research methods · 2023Article
- To reveal or to conceal: Appropriate statistical analysis is a moral obligation for authors in modern medicine.Indian journal of anaesthesia · 2023Article
- Ten simple rules for designing and conducting undergraduate replication projects.PLoS computational biology · 2023Article
- A survey on how preregistration affects the research workflow: better science but more work.Royal Society open science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 11 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton's ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations-as well as their statistical consequences-establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.
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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.