Format

Send to

Choose Destination
Eur J Epidemiol. 2016 Apr;31(4):337-50. doi: 10.1007/s10654-016-0149-3. Epub 2016 May 21.

Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.

Author information

1
Department of Epidemiology and Department of Statistics, University of California, Los Angeles, CA, USA. lesdomes@ucla.edu.
2
Competence Center for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg.
3
RTI Health Solutions, Research Triangle Institute, Research Triangle Park, NC, USA.
4
Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, School of Population Health, University of Melbourne, Melbourne, VIC, Australia.
5
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
6
Meta-Research Innovation Center, Departments of Medicine and of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA.
7
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

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.

KEYWORDS:

Confidence intervals; Hypothesis testing; Null testing; P value; Power; Significance tests; Statistical testing

Comment in

PMID:
27209009
PMCID:
PMC4877414
DOI:
10.1007/s10654-016-0149-3
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center