Statistical Significance
Also known as: Significant result, P-value, Significance level, Alpha
Statistical Significance refers to the probability that an observed result in a study occurred by chance rather than from a true effect. A result is typically considered statistically significant when the p-value is less than 0.05, meaning there is less than a 5% probability the finding is due to random variation.
Last updated: February 1, 2026
How Statistical Significance Works
The P-Value Explained
The p-value answers: “If there were no true effect, what’s the probability of seeing results this extreme?”
| P-value | Interpretation |
|---|---|
| p < 0.001 | Very strong evidence against no effect |
| p < 0.01 | Strong evidence |
| p < 0.05 | Moderate evidence (typical threshold) |
| p > 0.05 | Insufficient evidence |
What P < 0.05 Means
- If the drug truly had no effect…
- We would see results this extreme…
- Less than 5% of the time by chance alone
- Therefore, the effect is probably real
What P < 0.05 Does NOT Mean
- Does not mean 95% chance the drug works
- Does not indicate effect size or importance
- Does not prove the drug is clinically useful
- Does not mean the study was well-designed
Relevance to Peptides
Statistical Significance in Peptide Trials
STEP 1 Results (Semaglutide)
- Weight loss: -14.9% vs -2.4%
- P-value: under 0.001
- Interpretation: Extremely unlikely to be chance
SURMOUNT-1 Results (Tirzepatide)
- Weight loss: up to -22.5% vs -2.4%
- P-value: under 0.001
- Interpretation: Robust statistical evidence
Why Strong Significance Matters
Peptide trials typically show very low p-values because:
- Large sample sizes
- Substantial effect sizes
- Well-designed methodology
- Clear outcome measures
Statistical vs Clinical Significance
The Critical Distinction
| Concept | Question | Example |
|---|---|---|
| Statistical | Is it real? | P < 0.05 |
| Clinical | Does it matter? | 15% weight loss |
Example: Interpreting Both
Highly significant AND clinically meaningful:
- 15% weight loss, p < 0.001
- Real effect that matters to patients
Statistically significant but clinically marginal:
- 0.5% weight loss, p = 0.04
- Real but too small to matter
Not significant but potentially meaningful:
- 8% weight loss, p = 0.08
- Possibly real, needs larger study
Confidence Intervals
Complementing P-Values
| Element | Information Provided |
|---|---|
| P-value | Is effect likely real? |
| Confidence interval | Range of plausible effects |
| Effect size | Magnitude of difference |
Reading Confidence Intervals
“Weight loss: 15% (95% CI: 13-17%)”
- Best estimate: 15% weight loss
- We’re 95% confident true value is 13-17%
- If CI excludes zero, typically p < 0.05
Common Misconceptions
| Misconception | Reality |
|---|---|
| P = 0.05 is magical cutoff | It’s an arbitrary convention |
| Small p = large effect | P-value doesn’t measure effect size |
| P > 0.05 means no effect | May indicate insufficient power |
| Significant = important | Statistical ≠ clinical significance |
Frequently Asked Questions
Why is 0.05 the standard threshold?
It’s a historical convention, not a scientifically derived cutoff. Ronald Fisher proposed it as a reasonable threshold, and it became standard practice. Some fields use stricter thresholds (0.01, 0.001), and there’s ongoing debate about moving away from rigid cutoffs.
Can a large trial make tiny effects significant?
Yes. With enough participants, even tiny, clinically meaningless differences become statistically significant. This is why looking at effect size and confidence intervals matters as much as p-values. A statistically significant but trivial effect isn’t useful clinically.
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Disclaimer: This glossary entry is for educational purposes only and does not constitute medical advice. Always consult a qualified healthcare provider for medical questions.