[P value and confidence intervals: reporting and interpreting the result of a clinical study]

The main purpose of statistics in the analysis of clinical and epidemiological studies is to summarize data and information, as well as assess variability, trying to distinguish between chance findings and results that may be replicated upon repetition. Statistical analyses only convey the effect of chance element in data (random error). Statistics cannot control non-sampling errors concerning study design, conduct and methods adopted. At the end of the study, a result is defined statistically significant if the observed difference in the outcome variable is too large to be attributed to chance. A small P value provides evidence against the null hypothesis (of no effect), since data have been observed that would be unlikely if the null hypothesis was true. However, confidence intervals estimate separate the two data dimensions (strength of the relation between exposure and disease, and precision with which the relation is measured), and add to the hypothesis testing useful information for finding interpretation and further research.