stats

name	stats
description	Detects statistical errors, logical fallacies, and methodological issues in research content. Checks for p-hacking, correlation/causation confusion, underpowered samples, multiple comparisons problems, overgeneralization, and other common fallacies. Use when the user asks to validate statistics, audit quantitative claims, check methodology, or find logical errors. Returns minimal output on purely theoretical content — most useful after empirical data is present.
license	MIT
metadata	{"author":"archora","version":"1.0","website":"https://archora2026.com/"}

Statistical Validator

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.