Confidence and precision increase with high statistical power
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mHealth research will be particularly susceptible to research practices that degrade quality, consistency, generalizability, and therefore replicability and reproducibility of findings, as data sensors allow vast collection of big data samples vulnerable to publication bias, P-hacking, HARKing, and low power.
“Lastly, from a study design perspective, mHealth research will be particularly susceptible to a number of research practices, which have been shown to degrade the quality, consistency, generalizability, and therefore replicability and reproducibility of findings31 as data sensors in this research field allow for the vast collection of big data samples, which will be vulnerable to some of the “four”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [64]
Cites
Power failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641 · see citing works
Self-citationNosek (2012) · doi:10.1177/1745691612459058 · Cited by 1,301 · see citing works
Habenular α5 nicotinic receptor subunit signalling controls nicotine intake
Fowler, C. D., Lu, Q., Johnson, P. M., Marks, M. J., & Kenny, P. J. (2011) · Nature · doi:10.1038/nature09797 · Cited by 663 · see citing works
Ten ironic rules for non-statistical reviewers
Friston, K. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.018 · Cited by 347 · see citing works
Dissecting the genetic architecture of human personality.
Munafo, M., & Flint, J. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.07.007 · Cited by 138 · see citing works
Self-citationCandidate and non-candidate genes in behavior genetics
Jonathan Flint, C., & Munafo, M. R. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.07.005 · Cited by 85 · see citing works
High Impact = High Statistical Standards? Not Necessarily So
Tressoldi, P. E., Giofrè, D., Sella, F., & Cumming, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056180 · Cited by 81 · see citing works
Obtaining evidence by a single well-powered trial or several modestly powered trials
IntHout, J., Ioannidis, J. P. A., & Borm, G. F. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280212461098 · Cited by 73 · see citing works
Self-citationSmall sample size is not the real problem
Bacchetti, P. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3475-c3 · Cited by 54 · see citing works
Sample size and the fallacies of classical inference
Friston, K. J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.057 · Cited by 50 · see citing works
Ironing out the statistical wrinkles in “ten ironic rules”
Lindquist, M. A., Caffo, B. C., & Crainiceanu, C. M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.056 · Cited by 48 · see citing works
Optimal type I and type II error pairs when the available sample size is fixed
Ioannidis, J. P. A., Hozo, I., & Djulbegović, B. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.03.002 · Cited by 33 · see citing works
Self-citationMisuse of power: in defence of small-scale science
Quinlan, P. T. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3475-c1 · Cited by 26 · see citing works
Experimental power comes from powerful theories — the real problem in null hypothesis testing
Ashton, J. C. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3475-c2 · Cited by 15 · see citing works
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The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences
Hedge, C., Powell, G., & Sumner, P. (2018) · Behavior Research Methods · doi:10.3758/s13428-017-0935-1 · Cited by 1,457 · see citing works
Okon-Singer, H., Hendler, T., Pessoa, L., & Shackman, A. J. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00058 · Cited by 371 · see citing works
Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research
Nelson, B. W., Low, C. A., Jacobson, N., Areán, P., Torous, J., & Allen, N. B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0297-4 · Cited by 241 · see citing works
Low statistical power in biomedical science: a review of three human research domains
Dumas-Mallet, E., Button, K. S., Boraud, T., Gonon, F., & Munafò, M. R. (2017) · Royal Society Open Science · doi:10.1098/rsos.160254 · Cited by 208 · see citing works
Self-citationArticle Commentary: On the Persistence of Low Power in Psychological Science
Vankov, I., Bowers, J., & Munafò, M. R. (2014) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2014.885986 · Cited by 82 · see citing works
Self-citation
- Authors
- Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R.
- Affiliations
- University of Bristol (GB)
- Stanford University (US)
- Center for Open Science (US)
- Venue
- Nature Reviews Neuroscience
- Published
- 2013-07-03
- Volume
- 14
- Issue
- 8
- Pages
- 585-585
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- OpenAlex
- W2094312839
- Cited by
- 121
- References
- 15
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 23 · 2023: 10 · 2022: 5 · 2021: 13 · 2020: 8 · 2019: 7 · 2018: 7 · 2017: 13 · 2016: 8 · 2015: 5 · 2014: 6 · 2013: 1
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Explore Bristol Research (submittedVersion)
- Funders
- Wellcome Trust (090532)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- sample size, statistical power, small sample size, null hypothesis testing, confidence intervals, precision, effect size estimation, candidate genes, behavioral genetics, neuroimaging
- MeSH terms
- Humans, Neurosciences, Sample Size
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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