ArticleF1000Research2015
Null hypothesis significance testing: a short tutorial.
Article in F1000Research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of sample size determination in Bayesian randomized clinical trials: full Bayesian methods are rarely used.BMC medical research methodology · 2026Pooled it
- Mindfulness related changes in grey matter: a systematic review and meta-analysis.Brain imaging and behavior · 2021Pooled it
- Spatiotemporal Dynamics of Binocular Interaction in Anisometropic Amblyopia Revealed by Classification Image.Investigative ophthalmology & visual science · 2026Article
- When meta-analysis misleads: the need for methodological integrity in e-cigarette research.Internal and emergency medicine · 2025Article
- Research methods in genetic counseling: Statistical approaches and resources.Journal of genetic counseling · 2025Article
- Nonsignificance misinterpreted as an effect's absence in psychology: prevalence and temporal analyses.Royal Society open science · 2025Article
- Impacts of video communication on psychological well-being and cosmetic surgery acceptance.Computers in human behavior · 2023Article
- Advances in human intracranial electroencephalography research, guidelines and good practices.NeuroImage · 2022Review
- Quality Output Checklist and Content Assessment (QuOCCA): a new tool for assessing research quality and reproducibility.BMJ open · 2022Review
- Statistical data presentation: a primer for rheumatology researchers.Rheumatology international · 2021Review
- Investigation of the effect of dietary intake of omega-3 polyunsaturated fatty acids on trauma-induced white matter injury with quantitative diffusion MRI in mice.Journal of neuroscience research · 2020Article
- Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators.PloS one · 2020Article
- Basic statistical considerations for physiology: The journalTemperature (Austin, Tex.) · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short tutorial, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose reporting practices.
Indexed as
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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.