The drive for optimal clinical decisions based on "best" evidence has gained significant momentum in the last few decades. In parallel with the evidence-based medicine approach, various "hierarchy of evidence" stratifications have also emerged 1-3 . Overall, those stratifications attempt to characterize underlying bias (that is, validity) and emphasize the relative quality of different forms of evidence. In a traditional pyramidal representation (Figure 1) 2,3 , systematic reviews and meta-analyses of randomized clinical trials (rcts) represent the pinnacle of the pyramid, followed by individual rcts and observational studies (for example, case-control or cohort studies). Conversely, case reports and series, often called "anecdotal evidence," are placed at the bottom of the pyramid. Notwithstanding a number of limitations and criticisms 4 , including an overreliance on systematic reviews and meta-analyses, clinical decision-making continues to be guided by such "hierarchies of evidence." Increasingly, however, the evidence-based medicine approach-and thus clinical decision-making-is coming to rely on mathematical or statistical characterizations of biologic processes (for example, disease progression or relapse, patient survival, and quality of life).The applications of math and statistics in clinical medicine are numerous and include characterizations of biologic observations and mathematical modelling of health outcomes 5 . As examples of the former, the biologic observations encountered in rcts (such as disease progression or relapse, patient survival, and quality of life) are often represented by bio-statistical measures that include estimates of effect size and the uncertainties involved (for example, type i and ii errors, p values, and 95% confidence intervals) 6,7 . As well, meta-analyses often use mathematical measures that extrapolate efficacy from individual rcts to compute compound measures of effect size and uncertainty. Not surprisingly, for clinicians who chose a science path driven by human biology, the underlying math and statistics are not infrequently perceived as complex. Nonetheless, a working knowledge of the underlying statistical methods is necessary for clinicians to properly interpret the current evidence. Furthermore, knowledge that is more in-depth is also often required to properly interpret the clinical evidence and avoid the pitfalls of mere binary interpretation of study outcomes as simply positive or negative 6,7 .Clinical evidence that stems from biologic observations cannot address all pertinent gaps in knowledge. As an