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Wrangling with p ‐values versus effect sizes to improve medical decision‐making: A tutorial
Author(s) -
Kraemer Helena C.,
Neri Eric,
Spiegel David
Publication year - 2020
Publication title -
international journal of eating disorders
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.785
H-Index - 138
eISSN - 1098-108X
pISSN - 0276-3478
DOI - 10.1002/eat.23216
Subject(s) - mythology , number needed to treat , value (mathematics) , psychology , clinical trial , randomized controlled trial , medicine , actuarial science , statistics , relative risk , mathematics , surgery , philosophy , pathology , confidence interval , economics , theology
The most pervasive and damaging myth in clinical research is that the smaller the p ‐value, the stronger the hypothesis. In reality, the p ‐value primarily reflects the quality of research design decisions. The most common proposal to avoid misleading conclusions from clinical research requires the appropriate use of effect sizes, but which effect size, used when and how, is an open question. A solution is proposed for perhaps the most common problem in clinical research, the comparison between two populations, for example, comparison of two treatments in a randomized clinical trial or comparison of high risk versus low risk individuals in an epidemiological study: the success rate difference or equivalently the number needed to treat/take (NNT).