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STATISTICS, BIOMEDICINE AND SCIENTIFIC FRAUD

C. De Felice, A. Cortelazzo, S. Leoncini, C. Signorini, J. Hayek, L. Ciccoli
  • C. De Felice
    Neonatal Intensive Care Unit, University Hospital, Azienda Ospedaliera Universitaria Senese (AOUS), Policlinico “S. M. alle Scotte”, Siena, Italy | c.defelice@ao-siena.toscana.it
  • A. Cortelazzo
    Department of Medical Biotechnologies, University of Siena; Child Neuropsychiatry Unit, University Hospital, (AOUS), Policlinico “S.M. alle Scotte”, Siena, Italy
  • S. Leoncini
    Child Neuropsychiatry Unit, University Hospital, (AOUS), Policlinico “S.M. alle Scotte”, Siena; Department of Molecular and Developmental Medicine, University of Siena, Italy
  • C. Signorini
    Department of Molecular and Developmental Medicine, University of Siena, Italy
  • J. Hayek
    Child Neuropsychiatry Unit, University Hospital, (AOUS), Policlinico “S.M. alle Scotte”, Siena, Italy
  • L. Ciccoli
    Department of Molecular and Developmental Medicine, University of Siena, Italy

Abstract

A consistent fraction of published data on scientific journals is not reproducible mainly due to insufficient knowledge of statistical methods. Here, we discuss on the use of proper statistical tools in biomedical research and statistical pitfalls potentially undermining the scientific validity of published data. Apart from unaware errors, a growing concern exists regarding data fabrication and scientific misconduct. Indeed, the social impact of false scientific data can be largely unpredictable and devastating, as shown by the worldwide dramatic effects on vaccinations coverage following a retracted paper published on a highly authoritative medical journal. Unfortunately, statistics shows a quite limited power in detecting false science, although a few statistical tools, such as the Benford’s law, are known. Taken together, statistics in biomedical sciences i) is a powerful tool to interpret experimental data; ii) has limited power in detecting false science; and iii) first and foremost, is not the result of a simple “click of a mouse”, but should be the result of accurate research planning by experienced and knowledgeable users.

Keywords

Biomedical sciences; scientific fraud; scientific misconduct; statistics; statistical errors; statistical inference

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Submitted: 2016-11-23 10:48:07
Published: 2016-12-13 10:39:19
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Copyright (c) 2015 Claudio De Felice, Alessio Cortelazzo, Silvia Leoncini, Cinzia Signorini, Joussef Hayek, Lucia Ciccoli

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