For decades, the threshold of p < 0.05 served as the universal gold standard for scientific discovery. However, leading medical and life-science journals now mandate comprehensive statistical reporting including effect sizes, confidence intervals (CIs), and statistical power calculations.
1. The Difference Between Statistical Significance and Clinical Relevance
A statistically significant finding does not automatically imply a meaningful clinical or practical outcome. In large sample sizes, even trivial differences can produce a p-value below 0.001. Reviewers look for:
- Absolute and Relative Risk Reductions: Concrete metrics that convey real-world patient benefit.
- Number Needed to Treat (NNT): Clear indicators of therapeutic efficacy.
- 95% Confidence Intervals: Providing the precision and plausible range of the true population parameter.
Guideline: Never report a p-value in isolation. Pair every test statistic with its corresponding effect size (e.g., Cohen's d, odds ratio, hazard ratio) and 95% CI.
2. Avoiding P-Hacking and Multiple Testing Hazards
Conducting multiple post-hoc comparisons without mathematical adjustments drastically inflates Type I error rates. Always pre-register hypotheses or apply Bonferroni / Benjamini-Hochberg False Discovery Rate (FDR) corrections when evaluating large outcome matrices.
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