1{2 "verdict": "accept",3 "issues": [4 {"id": "R1", "severity": "minor", "location": "out/report.md (Uncertainty)", "issue": "Says the conventional shortfall (0.106) is 'about 13 MCSE under either SE definition'. That holds only for the SE at the estimated p (0.0081). With the SE at p=0.95 (0.0049), which is the convention used for the decision rule, the shortfall is about 22 MCSE.", "suggested_fix": "State both figures: ~13 MCSE using the estimated-p SE and ~22 MCSE using the SE at p=0.95."},5 {"id": "R2", "severity": "minor", "location": "out/report.md (Limitations)", "issue": "'Different seeds will move the coverages by roughly ±2 MCSE' is loose. Seed-to-seed variation is about ±1 MCSE (1 SD), and ±2 MCSE is roughly a 95% range.", "suggested_fix": "Rephrase as 'seed-to-seed variation has SD ≈ 1 MCSE (≈0.008 for conventional, ≈0.007 for HC1)'."}6 ],7 "summary": "Every required key is present. Re-running analysis.py in a scratch copy reproduced results.json exactly. The code uses only data/design.csv, keeps x and σ fixed, uses R=2000 with a fixed seed, and gets its constants from TASK.md. se_conv and se_HC1 match statsmodels to machine precision. The exact SD comes from the analytic formula sqrt(Σw_i²σ_i²). The undercoverage flag follows the 3-MCSE rule at p=0.95 (threshold 0.9354), and the report states that this convention was chosen. Coverage 0.844 vs 0.95 gives a robust undercoverage conclusion. The HC1 interval, the 29% downward bias of the conventional SE, and the agreement between MC and exact SD all check out. The data audit (no missing values, no duplicates, σ>0; corr(|x|,σ)≈0.96) is accurate. The only issues are two minor wording problems in the uncertainty statements."8}9