There is life beyond the statistical significance.

Area of Science:

• Biostatistics
• Health Services Research
• Clinical Epidemiology

Background:

• The traditional reliance on P-values for statistical significance in research is debated within the scientific community.
• Current practices often lead to misinterpretations of study results, particularly in health care delivery research.

Discussion:

• This article challenges the "tyranny of P-value" by advocating for a shift away from statistical significance as the sole interpretive criterion.
• It proposes the use of confidence intervals and exact P-values for more informative reporting.
• The integration of statistical and clinical significance is encouraged through minimal important differences and effect size, ideally using the GRADE approach.

Key Insights:

• Results should not be dichotomized as "statistically significant" or "statistically non-significant."
• Reporting should include point estimates with confidence intervals, even for potential harms, to convey a complete picture.
• Conclusions should incorporate multiple factors beyond P-values, including certainty of evidence, net benefit, economic factors, and patient values.

Outlook:

• Standardized reporting of research findings is crucial for improving scientific communication.
• Adopting a more comprehensive approach to interpreting results enhances the applicability and value of health care delivery research.
• There is a significant opportunity to move beyond statistical significance towards a more nuanced understanding of research outcomes.