Interpretations on time to move beyond P value

Sir,

The recent Letter to Editor by Yadav and Kumar¹ on simpler biostatistical approaches offers a necessary catalyst for re-evaluating how we interpret clinical evidence, published in the November 2025 issue of the Indian Journal of Medical Research. While their attempt to bridge the gap between complex statistics and clinical utility is commendable, the move toward even more stringent -value thresholds warrants a critical reappraisal to avoid repeating the methodological pitfalls of the past. The fundamental issue lies not in the choice as threshold but in the arbitrary dichotomization of scientific truth. As noted in the sources, a -value remains a measure of data compatibility with a specific statistical model; it is neither a surrogate for the probability of a hypothesis being true nor a metric for clinical magnitude. Universally lowering a significant surge in Type II errors, particularly in investigations of rare diseases or early-phase trials where large sample sizes are often an insurmountable hurdle. Such rigidity may inadvertently suppress modest but clinically vital findings, thereby amplifying publication bias toward only the most dramatic results. Furthermore, the transition from ROC-based assessments to metrics like Positive Predictive Value (PPV) and the P-index, while clinically intuitive, introduce significant transportability issues. These indices are inherently sensitive to disease prevalence, meaning their utility may dissolve when applied to diverse patient populations with varying prior probabilities. Instead of trading one single-point metric for another, we believe the path forward requires a multi-component inferential framework. This includes the routine integration of effect sizes, confidence intervals, and the minimum clinically important difference (MCID) to ground statistical findings in patient-oriented reality. By incorporating Bayesian approaches to quantify the actual probability of clinical success, we can move toward a more nuanced and scientifically sound landscape that prioritizes clinical judgment over numerical cut-offs. Last but not the least, as an analogy for clinical understanding: evaluating a treatment’s efficacy solely on a stringent value is like a physician attempting to diagnose a complex multisystem disease by looking only at a single laboratory value on a ‘pass/fail’ basis. Just as a clinician must integrate physical signs, patient history, and multiple diagnostic tests to form a complete picture, a researcher must look beyond the ‘statistical light’ of the P value to understand the whole topography of clinical evidence. This issue merits further investigation. We thank Yadav and Kumar¹ for their high-toned comment in the Indian J Med Res.