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Understanding and interpreting confidence and credible intervals around effect estimates
Open accessAbstract
Introduction Reporting confidence intervals in scientific articles is important and relevant for evidence-based practice. Clinicians should understand confidence intervals in order to determine if they can realistically expect results similar to those presented in research studies when they implement the scientific evidence in clinical practice. The aims of this masterclass are: (1) to discuss confidence intervals around effect estimates; (2) to understand confidence intervals estimation (frequentist and Bayesian approaches); and (3) to interpret such uncertainty measures.Content Confidence intervals are measures of uncertainty around effect estimates. Interpretation of the frequentist 95% confidence interval: we can be 95% confident that the true (unknown) estimate would lie within the lower and upper limits of the interval, based on hypothesized repeats of the experiment. Many researchers and health professionals oversimplify the interpretation of the frequentist 95% confidence interval by dichotomizing it in statistically significant or non-statistically significant, hampering a proper discussion on the values, the width (precision) and the practical implications of such interval. Interpretation of the Bayesian 95% confidence interval (which is known as credible interval): there is a 95% probability that the true (unknown) estimate would lie within the interval, given the evidence provided by the observed data.Conclusions The use and reporting of confidence intervals should be encouraged in all scientific articles. Clinicians should consider using the interpretation, relevance and applicability of confidence intervals in real-world decision-making. Training and education may enhance knowledge and skills related to estimating, understanding and interpreting uncertainty measures, reducing the barriers for their use under either frequentist or Bayesian approaches.
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“As such, a 95% HDI can be interpreted as a 95% probability that the true (unknown) estimate lies within the interval, given the observed data and priors [304].”
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Sontrop, J. M., Dixon, S. N., Garg, A. X., Jiménez, I. B., Dohein, O., Huang, S. S., & Clark, W. F. (2013) · American Journal of Nephrology · doi:10.1159/000350377 · Cited by 5,903
Probable Inference, the Law of Succession, and Statistical Inference
Wilson, E. B. (1927) · Journal of the American Statistical Association · doi:10.1080/01621459.1927.10502953 · Cited by 4,476
Confidence intervals rather than P values: estimation rather than hypothesis testing.
Gardner, M. J., & Altman, D. G. (1986) · BMJ · doi:10.1136/bmj.292.6522.746 · Cited by 2,014
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide
Twisk, J. W. R. (2003) · Cited by 1,244
The fickle P value generates irreproducible results
Halsey, L. G., Curran‐Everett, D., Vowler, S. L., & Drummond, G. B. (2015) · Nature Methods · doi:10.1038/nmeth.3288 · Cited by 787
Morris, J., & Gardner, M. J. (1988) · BMJ · doi:10.1136/bmj.296.6632.1313 · Cited by 764
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide
Twisk, J. W. R. (2013) · doi:10.1017/cbo9781139342834 · Cited by 593
Lesaffre, E. M. E. H., & Lawson, A. B. (2012) · Wiley eBooks · doi:10.1002/9781119942412 · Cited by 304
Statistical Inference by Confidence Intervals: Issues of Interpretation and Utilization
Sim, J., & Reid, N. G. (1999) · Physical Therapy · doi:10.1093/ptj/79.2.186 · Cited by 167
O’Neill, P. D. (2002) · Mathematical Biosciences · doi:10.1016/s0025-5564(02)00109-8 · Cited by 161
Research works printed but not linked: 1
- Newcombe, 1998, Interval estimation for the difference between independent proportions: comparison of eleven methods, Stat Med, 17, 873, 10.1002/(SICI)1097-0258(19980430)17:8<873::AID-SIM779>3.0.CO;2-I · DOI · Paper page
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Metadata
- Authors
- Hespanhol, L., Vallio, C. S., Costa, L. M., & Saragiotto, B. T.
- Affiliations
- Amsterdam UMC Location University of Amsterdam (NL)
- Universidade Cidade de São Paulo (BR)
- Corresponding author
- Hespanhol, Luiz
- Venue
- Brazilian Journal of Physical Therapy
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 23
- Issue
- 4
- Pages
- 290-301
- ISSN
- 1413-3555 (ISSN-L)
- PMC
- PMC6630113
- OpenAlex
- W2908045944
- Cited by
- 390
- References
- 25
- Citations by year
- 2026: 63 · 2025: 93 · 2024: 76 · 2023: 54 · 2022: 59 · 2021: 32 · 2020: 12 · 2019: 1
- FWCI
- 8.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Brazilian Journal of Physical Therapy (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo (2016/09220-1, 2016/24217-7, 2017/11665-4)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- confidence intervals, credible intervals, effect estimates, uncertainty quantification, frequentist interpretation, Bayesian interpretation, statistical significance, clinical decision-making, evidence-based practice
- MeSH terms
- Bayes Theorem, Humans, Uncertainty
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