The misunderstood P-value: why statistical significance is not enough in clinical practice

Area of Science:

• Anaesthesiology
• Clinical Research Methodology
• Statistical Inference

Background:

• P-values are traditionally used to interpret clinical research findings.
• Over-reliance on P-values can lead to misinterpretation and flawed clinical decisions.
• The limitations of P-values necessitate alternative or supplementary statistical approaches.

Discussion:

• This article addresses common misconceptions surrounding P-values in scientific literature.
• It advocates for a multifaceted approach to data interpretation beyond traditional P-value thresholds.
• Integrating the minimum clinically important difference (MCID) is crucial for clinical relevance.

Key Insights:

• Effect sizes and Bayesian methods offer valuable complementary information to P-values.
• Minimum clinically important difference (MCID) provides context for the practical significance of research findings.
• A comprehensive evaluation of research requires considering statistical significance alongside clinical importance.

Outlook:

• Future clinical research should prioritize a balanced interpretation of statistical and clinical significance.
• Adoption of diverse statistical metrics will enhance evidence-based practice in anaesthesiology.
• Improved statistical literacy among clinicians will foster more informed patient care decisions.