There is life beyond the statistical significance

Abstract

This article challenges the "tyranny of P-value" and promote more valuable and applicable interpretations of the results of research on health care delivery. We provide here solid arguments to retire statistical significance as the unique way to interpret results, after presenting the current state of the debate inside the scientific community. Instead, we promote reporting the much more informative confidence intervals and eventually adding exact P-values. We also provide some clues to integrate statistical and clinical significance by referring to minimal important differences and integrating the effect size of an intervention and the certainty of evidence ideally using the GRADE approach. We have argued against interpreting or reporting results as statistically significant or statistically non-significant. We recommend showing important clinical benefits with their confidence intervals in cases of point estimates compatible with results benefits and even important harms. It seems fair to report the point estimate and the more likely values along with a very clear statement of the implications of extremes of the intervals. We recommend drawing conclusions, considering the multiple factors besides P-values such as certainty of the evidence for each outcome, net benefit, economic considerations and values and preferences. We use several examples and figures to illustrate different scenarios and further suggest a wording to standardize the reporting. Several statistical measures have a role in the scientific communication of studies, but it is time to understand that there is life beyond the statistical significance. There is a great opportunity for improvement towards a more complete interpretation and to a more standardized reporting.

Fig. 1

Probability density function of the difference between two sample means. The point estimate is the most likely value of the parameter of interest across the CI. A confidence interval (CI) is a range of values used to estimate a population parameter and is associated with a specific confidence. With a CI of 95% confidence, there is a 95% probability that any given CI will contain the true population parameter and a 5% chance that it won’t (two tails of 2.5%)

Fig. 2

Interpretation of results for different scenarios according to statistical and clinical thresholds or minimal important difference (MID). Minimal (clinically) important difference (MID). The blue squares indicate the point estimate of the effect of a new treatment compared with a standard treatment, and the blue lines on either side of it the 95% confidence interval