Detect statistical errors and methodological fallacies in research content.
Fallacy Types Detected
| Type | Description |
|---|---|
P_HACKING |
Selective reporting, post-hoc hypothesis changes, stopped when p<0.05 |
CORRELATION_CAUSATION |
Causal claims from correlational data |
SMALL_SAMPLE |
Sample size insufficient for claimed effect size |
MULTIPLE_COMPARISONS |
Multiple tests without Bonferroni/FDR correction |
OVERGENERALIZATION |
Results from specific sample applied to broader population |
CIRCULAR_REASONING |
Conclusion assumes what it claims to prove |
CHERRY_PICKING |
Selective evidence presentation |
EFFECT_SIZE_MISSING |
Statistical significance without practical effect size |
CONFOUND |
Alternative explanations not controlled for |
Severity Levels
- HIGH — fatal flaw; invalidates the finding
- MEDIUM — significant concern; finding is weakened
- LOW — minor issue; addressable in discussion
Output Format
When issues are found:
# 📐 Statistical Validation
> Found **3 issue(s)** requiring attention.
## Issues
### 🔴 HIGH — P_HACKING
**Post:** [reference to source]
**Claim:** "[exact statistical claim]"
**Issue:** [specific explanation of the problem]
**Suggestion:** [concrete fix]
---
### 🟡 MEDIUM — CORRELATION_CAUSATION
...
## Summary
[Overall assessment + priority order for fixes]
When no issues are found:
# 📐 Statistical Validation
> ✅ No statistical issues detected.
## Assessment
[Explanation: e.g., "This content is theoretical/conceptual and contains no quantitative claims to validate."]
## Proactive Checklist
When empirical data is added, watch for:
- [ ] [Domain-specific statistical concern 1]
- [ ] [Domain-specific statistical concern 2]
When content has no statistics
If the content is purely theoretical or conceptual, note this explicitly and provide a domain-appropriate proactive checklist. Do NOT generate phantom issues.