Open AccessJournal Article
Statistical tests, P-values, confidence intervals, and power: a guide to misinterpretations
Sander Greenland,Stephen Senn,Kenneth J. Rothman,John B. Carlin,Charles Poole,Steven N. Goodman,Douglas G. Altman +6 more
TLDR
This paper provided definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions, and provided an explanatory list of 25 misinterpretations of P values, confidence intervals, and power.Abstract:
Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so-and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.read more
Citations
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Sensitivity Analysis in Observational Research: Introducing the E-Value
Tyler J. VanderWeele,Peng Ding +1 more
TL;DR: An important approach to evaluating evidence for causation in the face of unmeasured confounding is sensitivity analysis (or bias analysis), and it is proposed that observational studies start reporting the E-value, a new measure related to evidence for causality.
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A manifesto for reproducible science
Marcus R. Munafò,Brian A. Nosek,Brian A. Nosek,Dorothy V. M. Bishop,Katherine S. Button,Christopher D. Chambers,Nathalie Percie du Sert,Uri Simonsohn,Eric-Jan Wagenmakers,Jennifer J. Ware,John P. A. Ioannidis +10 more
TL;DR: This work argues for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives, in the hope that this will facilitate action toward improving the transparency, reproducible and efficiency of scientific research.
Journal ArticleDOI
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications.
Eric-Jan Wagenmakers,Maarten Marsman,Tahira Jamil,Alexander Ly,Josine Verhagen,Jonathon Love,Ravi Selker,Quentin Frederik Gronau,Martin Šmíra,Sacha Epskamp,Dora Matzke,Jeffrey N. Rouder,Richard D. Morey +12 more
TL;DR: Ten prominent advantages of the Bayesian approach are outlined, and several objections to Bayesian hypothesis testing are countered.
Journal ArticleDOI
Control of Confounding and Reporting of Results in Causal Inference Studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals
David J. Lederer,Scott C. Bell,Richard D. Branson,James D. Chalmers,Rachel Marshall,David M. Maslove,David Ost,Naresh M. Punjabi,Michael Schatz,Alan R. Smyth,Paul W. Stewart,Samy Suissa,Alex A. Adjei,Cezmi A. Akdis,Elie Azoulay,Jan Bakker,Jan Bakker,Jan Bakker,Zuhair K. Ballas,Philip G. Bardin,Esther Barreiro,Rinaldo Bellomo,Jonathan A. Bernstein,Vito Brusasco,Timothy G. Buchman,Sudhansu Chokroverty,Nancy A. Collop,James D. Crapo,Dominic A. Fitzgerald,Lauren Hale,Nicholas Hart,Felix J.F. Herth,Theodore J. Iwashyna,Gisli Jenkins,Martin Kolb,Guy B. Marks,Peter J. Mazzone,J. Randall Moorman,Thomas M. Murphy,Terry L. Noah,Paul N. Reynolds,Dieter Riemann,Richard Russell,Richard Russell,Aziz Sheikh,Giovanni Sotgiu,Erik R. Swenson,Rhonda D. Szczesniak,Rhonda D. Szczesniak,Ronald Szymusiak,Jean-Louis Teboul,Jean Louis Vincent +51 more
TL;DR: Control of Confounding and Reporting of Results in Causal Inference Studies Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals is published.
Journal ArticleDOI
The Rotterdam Study: 2018 update on objectives, design and main results.
M. Arfan Ikram,Guy Brusselle,Guy Brusselle,Sarwa Darwish Murad,Cornelia M. van Duijn,Oscar H. Franco,André Goedegebure,Caroline C W Klaver,Tamar Nijsten,Robin P. Peeters,Bruno H. Stricker,Henning Tiemeier,André G. Uitterlinden,Meike W. Vernooij,Albert Hofman,Albert Hofman +15 more
TL;DR: The rationale of the study and its design is given, a summary of the major findings and an update of the objectives and methods are presented and the cohort is being expanded by persons aged 40 years and over.
References
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Book
Judgment Under Uncertainty: Heuristics and Biases
Amos Tversky,Daniel Kahneman +1 more
TL;DR: The authors described three heuristics that are employed in making judgements under uncertainty: representativeness, availability of instances or scenarios, and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.
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The environment and disease: association or causation?
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