Direct comparison
Statistical vs. Clinical Significance
Why a significant p-value can be clinically meaningless, and how effect size, MCID, and NNT determine real-world treatment impact.
Side-by-side comparison
| Dimension | Statistical Significance | Clinical Significance |
|---|---|---|
| What it answers | Is the observed effect unlikely to be due to chance? | Is the effect large enough to matter to a patient? |
| Core metric | p-value, compared against a pre-specified alpha (conventionally 0.05) | Effect size (Cohen's d, risk ratio, absolute risk reduction) compared against a Minimal Clinically Important Difference (MCID) |
| Depends heavily on | Sample size — large samples can make trivial effects statistically significant | The specific outcome measure and population — MCIDs are instrument- and condition-specific, not universal |
| Origin | Fisher's significance testing (1920s); Neyman-Pearson hypothesis testing framework (1928–1933) | Jaeschke, Singer & Guyatt, 1989 (MCID concept, originating in respiratory/quality-of-life research) |
| Reported as | p-value or confidence interval around the null | Effect size with confidence interval, and/or Number Needed to Treat (NNT) |
| Common failure mode | Statistically significant but trivial effect in a very large trial | Clinically important effect that misses significance in an underpowered trial (Type II error risk) |
| What a null result means | Failed to reject H0 — not proof the null hypothesis is true | A small or absent effect estimate, OR an underpowered study — the confidence interval distinguishes which |
| Who defines the threshold | Statistician / protocol, as a fixed convention (alpha) | Patients and clinicians, empirically, via anchor-based or distribution-based MCID studies |
| Reporting standard | p-values required but insufficient alone under CONSORT 2010 | CONSORT 2010 calls for effect size + precision (CI) alongside the p-value for every primary/secondary outcome |
Common questions
FAQ
Can a result be both statistically and clinically significant?+
Yes — this is the ideal case, and the norm for treatments with genuinely large effects tested in appropriately sized trials. The distinction matters specifically because the two can diverge, not because they usually do.
Does a non-significant p-value mean a treatment doesn't work?+
No. 'Failed to reject the null hypothesis' is not the same as 'proved the null hypothesis true.' A non-significant result in an underpowered study is consistent with either a genuinely small/absent effect or a real effect the study lacked power to detect.
Which one should a regulator or clinician weigh more heavily?+
Both, for different questions: statistical significance establishes the effect probably isn't chance; clinical significance (effect size against MCID, weighed against risk/cost/burden) establishes whether it's worth acting on.
