1{2 "verdict": "accept",3 "issues": [4 {5 "id": "R1",6 "severity": "minor",7 "location": "out/report.md (Uncertainty); out/claims.json C4",8 "issue": "Using 1.358/sqrt(n) when the mean and SD are estimated is conservative only for detecting non-normality. For the claim that the baseline sits 'below' that threshold it is lenient, because the correct Lilliefors critical value (~0.886/sqrt(n) ≈ 0.0028) is smaller. The baseline KS of 0.0017 is below both, so the conclusion still holds, but the wording is one-sided.",9 "suggested_fix": "Give the Lilliefors critical value (~0.886/sqrt(n)) or a simulated null quantile, or note that the conservativeness applies in one direction only."10 },11 {12 "id": "R2",13 "severity": "minor",14 "location": "out/report.md (Data and exclusions)",15 "issue": "The report cites SHA-256 checks against bundle.json, which is not a data/ input. This has no effect on the results because analysis.py reads only data/.",16 "suggested_fix": "Say that the hash check was done outside the analysis, or drop the mention."17 }18 ],19 "summary": "I re-ran analysis.py in a scratch copy and it reproduces results.json and counterexample.csv byte-for-byte (n=100000, seed 20261029, design followed, reads only data/). All required keys are present and correct. The verdicts are A false and B true. The counterexample is Y=S·X with S an independent random sign. It is valid: the marginals are exactly N(0,1) by symmetry, and X+Y = ½δ0 + ½N(0,4) is neither continuous nor constant, so it is not normal even under A's degenerate allowance. The argument explains why Y=-X fails. The KS value 0.2508 matches the theoretical 0.25, half of the sums are exactly 0, and the independent baseline (0.0017) is present. The claims are appropriately hedged: the simulation is presented as illustration, not proof. Only minor wording issues remain."20}21