Clinical relevance is commonly assessed as a result of an intervention. Nevertheless, it can be also extended to any other non experimental study design types, for instance, for cross-sectional studies.
To sum up, both significances (statistical and clinical) are not mutually exclusive but complementary in reporting results of clinical research. Researchers should abandon the only use of the p-value interpretation. Here you have a starting point for the evaluation of the clinical relevance.
Most of the times, results coming from a research project – specifically in the health sciences field – use statistical significance to show differences or associations among groups in the variables of interest. Setting up the null hypothesis as no difference between groups and the alternative showing just the opposite –i.e, there is a relationship between the analyzed factors –, and after performing the required statistical method, a p-value is provided. This p-value indicates, under an established threshold of significance (say, Type I or alpha error), the strength of the evidence against the null hypothesis. If the p-value is lower than alpha, results lead to a statistically significant conclusion; otherwise, there is no statistical significance.
According to my personal and other biostatisticians’ experience in the medical area, most of physicians are only interested in the statistical significance of their main objectives. They only want to know whether the p-value
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