Beyond P Values: The Urgent Need for Biologically Informed Statistical Thresholds in Regenerative Medicine

Dear Editor, Regenerative medicine represents a paradigm shift in modern biomedical science, focusing not merely on symptom management but on restoration of tissue structure and function. However, despite rapid advances in stem cell therapy, extracellular vesicle applications, and tissue engineering, the statistical frameworks used to evaluate these interventions remain largely unchanged from traditional biomedical research. In particular, the continued reliance on conventional P value thresholds warrants critical re-evaluation. The P value, historically introduced as a measure of incompatibility between observed data and a null hypothesis, has become the dominant criterion for determining “statistical significance” in biomedical research. Nevertheless, it is now widely acknowledged that P values are frequently misunderstood and misused, often leading to misleading conclusions and overinterpretation of results.[1] Importantly, a statistically significant result does not necessarily imply biological or clinical relevance, as P values do not convey effect size or the magnitude of therapeutic benefit.[2] This limitation is particularly critical in regenerative medicine, where therapeutic outcomes – such as tissue regeneration, angiogenesis, or functional recovery – are inherently complex, nonlinear, and time-dependent. In such contexts, small statistical differences may reach significance due to large sample sizes, yet fail to translate into meaningful biological improvement. Conversely, potentially transformative regenerative effects may be overlooked due to insufficient statistical power or rigid adherence to arbitrary thresholds such as α = 0.05, which itself lacks a universal scientific justification.[3] Emerging evidence further highlights that reliance on fixed significance levels can distort scientific interpretation. For instance, lowering the significance threshold may reduce false-positive findings but simultaneously increase false negatives, potentially obscuring true therapeutic effects.[4] Moreover, only a proportion of statistically significant findings in biomedical research adequately address clinical relevance, underscoring a critical disconnect between statistical outcomes and real-world applicability.[5] In regenerative medicine, where biological plausibility and functional restoration are paramount, it is imperative to move beyond binary interpretations of significance. We propose the integration of biologically informed statistical thresholds, incorporating parameters such as minimum clinically important difference, effect size estimation, and probabilistic frameworks. Such approaches would allow researchers to evaluate not only whether an effect exists but also whether it is sufficiently meaningful to justify clinical translation. Furthermore, the adoption of continuous evidence measures – rather than dichotomous “significant/non-significant” classifications – could enhance reproducibility and reduce publication bias. The American Statistical Association has similarly emphasized that P values should not be used in isolation and must be interpreted within the broader context of study design, data quality, and prior evidence. In conclusion, regenerative medicine demands a statistical paradigm aligned with its biological complexity. Moving beyond rigid P value thresholds toward biologically grounded, context-aware statistical inference is not merely methodological refinement – it is a necessity for advancing translational impact and improving patient outcomes. We encourage journals, reviewers, and researchers to reconsider current statistical practices and adopt more integrative, biologically meaningful approaches. Author contributions B.V. solely contributed to the conceptualization, literature review, drafting, revision, and final approval of the manuscript. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.