Health significance and statistical uncertainty. The value of P-value

The P-value is widely used as a summary statistics of scientific results. Unfortunately, there is a widespread tendency to dichotomize its value in "P<0.05" (defined as "statistically significant") and "P>0.05" ("statistically not significant"), with the former implying a "positive" result and the latter a "negative" one.

To show the unsuitability of such an approach when evaluating the effects of environmental and occupational risk factors.

We provide examples of distorted use of P-value and of the negative consequences for science and public health of such a black-and-white vision.

The rigid interpretation of P-value as a dichotomy favors the confusion between health relevance and statistical significance, discourages thoughtful thinking, and distorts attention from what really matters, the health significance.

A much better way to express and communicate scientific results involves reporting effect estimates (e.g., risks, risks ratios or risk differences) and their confidence intervals (CI), which summarize and convey both health significance and statistical uncertainty. Unfortunately, many researchers do not usually consider the whole interval of CI but only examine if it includes the null-value, therefore degrading this procedure to the same P-value dichotomy (statistical significance or not).

In reporting statistical results of scientific research present effects estimates with their confidence intervals and do not qualify the P-value as "significant" or "not significant".