The medical field has historically resisted uncertainty, often delaying the integration of new scientific evidence into clinical practice—sometimes by nearly two decades. This inertia reflects deep-rooted cultural and epistemological barriers that also impede the adoption of innovations such as Artificial Intelligence (AI). Yet, the call for more rigorous decision-making in medicine is not new. In the 18th century, Pierre-Simon Laplace emphasized the value of probability theory in clinical reasoning, a view later echoed by William Osler, who famously described medicine as “the science of uncertainty and the art of probability.” These early insights gained traction through the work of Sir Austin Bradford Hill and Archibald Cochrane, whose contributions laid the groundwork for Evidence-Based Practice (EBP). In the 1990s, Gordon Guyatt formally introduced Evidence-Based Medicine (EBM), advocating for clinical decisions grounded in empirical data, professional expertise, and patient values. In this evolving landscape, basic statistical literacy is no longer sufficient. In this context, cultivating probabilistic reasoning and statistical thinking has become essential to support ethically sound and evidence-aligned decisions to guide a meaningful transformation in both clinical training and practice.