Clinical vs. statistical significance: considerations for clinical laboratories.

Amongst the main perspectives when evaluating the results of medical studies are statistical significance (following formal statistical testing) and clinical significance. While statistical significance shows that a factor's observed effect on the study results is unlikely (for a given alpha) to be due to chance, effect size shows that the factor's effect is substantial enough to be clinically useful. The essence of statistical significance is "negative" - that the effect of a factor under study probably did not happen by chance. In contrast, effect size and clinical significance evaluate whether a clinically "positive" effect of a factor is effective and cost-effective. Medical diagnoses and treatments should never be based on the results of a single study. Results from numerous well-designed studies performed in different circumstances are needed, focusing on the magnitude of the effects observed and their relevance to the medical matters being studied rather than on the p-values. This paper discusses statistical inference and its relevance to clinical importance of quantitative testing in clinical laboratories. To achieve this, we first pose questions focusing on fundamental statistical concepts and their relationship to clinical significance. The paper also aims to provide examples of using the methodological approaches of superiority, equivalence, non-inferiority, and inferiority studies in clinical laboratories, which can be used in evidence-based decision-making processes for laboratory professionals.