Insights in Hypothesis Testing and Making Decisions in Biomedical Research

Varin Sacha1, Demosthenes B. Panagiotakos2, *
1 Collège de Villamont, Lausanne, Switzerland
2 School of Health Science and Education, Harokopio University, Athens, Greece

Article Metrics

CrossRef Citations:
Total Statistics:

Full-Text HTML Views: 1304
Abstract HTML Views: 546
PDF Downloads: 368
ePub Downloads: 239
Total Views/Downloads: 2457
Unique Statistics:

Full-Text HTML Views: 718
Abstract HTML Views: 314
PDF Downloads: 266
ePub Downloads: 175
Total Views/Downloads: 1473

Creative Commons License
© Sacha et al.; Licensee Bentham Open

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the 46 Paleon Polemiston St. Glyfada, Attica, 166 74, Greece; Tel.: +30210-9603116; Fax: +30210-9600719; E-mail:


It is a fact that p values are commonly used for inference in biomedical and other social fields of research. Unfortunately, the role of p value is very often misused and misinterpreted; that is why it has been recommended the use of resampling methods, like the bootstrap method, to calculate the confidence interval, which provides more robust results for inference than does p value. In this review a discussion is made about the use of p values through hypothesis testing and its alternatives using resampling methods to develop confidence intervals of the tested statistic or effect measure.

Keywords: Biostatistics, Confidence intervals, Evidence based medicine, p value, Resampling.