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.
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