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From archora-research
Detects statistical errors, logical fallacies, and methodological issues in research content. Use for validating statistics, auditing quantitative claims, or checking methodology.
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Detect statistical errors and methodological fallacies in research content.
Evaluates scientific claims and evidence quality using GRADE and Cochrane Risk of Bias frameworks. Assesses experimental design, biases, confounders, and statistical validity.
Validates CSV/TSV/Excel files and data analyses for quality, completeness, uniqueness, accuracy, consistency, outliers, and bias using qsv stats and frequency tools.
QA data analyses for methodology, accuracy, biases, and pitfalls before stakeholder sharing. Spot-checks calculations, SQL results, visualizations, and conclusions.
Share bugs, ideas, or general feedback.
Detect statistical errors and methodological fallacies in research content.
| 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 |
# 📐 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]
# 📐 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]
If the content is purely theoretical or conceptual, note this explicitly and provide a domain-appropriate proactive checklist. Do NOT generate phantom issues.