feat(rvw): Implement RVW V2.0 Data Forensics Module - Day 6 StatValidator

Summary:
- Implement L2 Statistical Validator (CI-P consistency, T-test reverse)
- Implement L2.5 Consistency Forensics (SE Triangle, SD>Mean check)
- Add error/warning severity classification with tolerance thresholds
- Support 5+ CI formats parsing (parentheses, brackets, 95% CI prefix)
- Complete Python forensics service (types, config, validator, extractor)

V2.0 Development Progress (Week 2 Day 6):
- Day 1-5: Python service setup, Word table extraction, L1 arithmetic validator
- Day 6: L2 StatValidator + L2.5 consistency forensics (promoted from V2.1)

Test Results:
- Unit tests: 4/4 passed (CI-P, SE Triangle, SD>Mean, T-test)
- Real document tests: 5/5 successful, 2 reasonable WARNINGs

Status: Day 6 completed, ready for Day 7 (Skills Framework)
Co-authored-by: Cursor <cursoragent@cursor.com>

extraction_service/requirements-prod.txt

@@ -12,6 +12,7 @@ python-multipart==0.0.6
 pandas>=2.0.0
 numpy>=1.24.0
 polars>=0.19.0
+scipy>=1.11.0               # 统计验证（RVW V2.0 数据侦探：T检验、卡方检验）
 
 # PDF处理 - 使用 pymupdf4llm（替代 nougat，更轻量）
 PyMuPDF>=1.24.0             # PDF 核心库（代码中 import fitz 使用）