Pitfalls and misuse in data analysis

被引:0
|
作者
Supplisson, Olivier [1 ,2 ]
Sofonea, Mircea T. [3 ,4 ]
机构
[1] Coll France, CNRS, INSERM, CIRB, Paris, France
[2] Sorbonne Univ, Paris, France
[3] Univ Montpellier, INSERM, PCCEI, EFS, Montpellier, France
[4] CHU Nimes, Nimes, France
来源
ANESTHESIE & REANIMATION | 2023年 / 9卷 / 5-6期
关键词
Methodology; Statistical myths; Fallacies; Reproducibility; Bias; Significance; CONFIDENCE-INTERVAL; MEASUREMENT ERROR; MODEL-SELECTION; P-VALUES; STATISTICAL SIGNIFICANCE; MISSING DATA; EFFECT SIZE; ECOLOGY; GUIDE; NEUROSCIENCE;
D O I
10.1016/j.anrea.2023.08.002
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Data analysis is one of the cornerstones of biomedical research and evidence-based medicine. However, the conclusions it produces and the body of health applications derived from it are undermined by a variety of statistical pitfalls, common errors, and tolerated methodological malpractice. The persistence of these pitfalls in the literature alters the nature of interpretations and acts as a brake on scientific discoveries and consensus. In the context of a massive and growing flow of publications and non-expertised results (pre-publications, communications on social networks), methodological rigor and solid statistical training are more than ever the best bulwarks against a crisis of reproducibility and confidence. In this article, we draw on recent methodological literature to review the main methodological pitfalls, be they well-known ones such as those relating to the significance or the multiplicity of comparisons, or less familiar ones such as dichotomisation, variable selection, or problems of spatial and temporal auto-correlation. This approach is part of a long series of refinements and reminders initiated several decades ago by the methodological community to maintain a level of control and criticism that is essential to the reliability of biomedical research.
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页码:440 / 450
页数:11
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