[Clinical significance vs statistical significance. How to interpret the confidence interval at 95].

The validity of a study depends on its proper planning, execution and analysis. If these are sufficiently correct, the decision to apply the recommendations issued depends on the expected clinical effect. This effect may have random variations, hence the need to use statistical inference. For years the p-value has been used to determine this statistical significance and the confidence intervals to measure the magnitude of the effect. In this review we present a proposal of how to interpret the 95 % confidence intervals (CI 95 %) as estimators of the expected effect variability based on considering the threshold or value of clinical significance and the null value of the difference or rejection of statistical significance. Thus, an association or effect where the CI 95 % includes the null value (no effect or difference) is interpreted as inconclusive; one between the null value and the clinical threshold (without including them) as possibly inconsequential; one that does not include the null value but the clinical threshold as yet not conclusive and one beyond the clinical threshold as conclusive.