In evidence-based medicine, statistical information is critical for investigators to interpret observations and make treatment recommendations. A voice of dissent opposing over-reliance on p-value based decision making — the widely accepted and overly practiced method for analyzing clinical trial data — is becoming stronger in the research fraternity. Several recent publications in reputed journals are questioning the popularity of the concept of ‘’statistical significance.”
The p-value was introduced to statistics not as a definitive test but as a tool to judge the probability of evidence gathered from an experiment holding true when the experiment is repeated. In brief, p-values range from 0 to 1; and the lower the value, the lower the probability of the results being attributed to pure chance. Conventionally, a p-value of 0.05 is the threshold to determine reliability, and consequently, publication-worthiness. This threshold, nevertheless, is random and the p-value is essentially more of a pragmatic tool, which when combined with background knowledge, could lead to a better scientific understanding. In fact, Regina Nuzzo, a professor at Gallaudet University, in her award-wining article opines that the magical 0.05 is a boundary too permeable to be taken seriously, as adding some extra data can change an effect from being significant to non-significant.