{"abstract":"Intervals are wrong whenever treatment and control means differ.","category":"Experiment statistics","checks":8,"contract":"L = ln(mean_t / mean_c) with standard error sqrt((se_t / mean_t)^2 + (se_c / mean_c)^2). Report the percent change 100 (e^L - 1) and the interval 100 (e^(L -/+ z se) - 1), each rounded to 4. Nonpositive means -> None.","evaluation_group":"w2-experiment-statistics-log-ratio-interval","failed_approach":"Adding relative standard errors before squaring overstates the variance.","family":"w2-experiment-statistics-log-ratio-interval-relative-standard-errors","id":"FA-74831","implementations":{"attempt":{"sha256":"8fec8ed7ae6f935a58b85adecd4ccdc3c8c31d039e4ec90b732c403298a6187d","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(mean_c, se_c, mean_t, se_t, z):\n    if mean_c <= 0 or mean_t <= 0:\n        return None\n    L = math.log(mean_t / mean_c)\n    se = math.sqrt((se_t / mean_t + se_c / mean_c) ** 2)\n    return [round(100 * (math.exp(L) - 1), 4), round(100 * (math.exp(L - z * se) - 1), 4), round(100 * (math.exp(L + z * se) - 1), 4)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 1', [10.0, 0.2, 10.0, 2.0, 1.645], [0.0, -28.1537, 39.186]),\n  ('summary sample 2', [10.0, 1.0, 1.8, 0.3, 1.645], [-82.0, -86.9258, -75.2184]),\n  ('summary sample 3', [2.0, 0.5, 1.8, 0.5, 1.96], [-10.0, -56.7354, 87.2201]),\n  ('summary sample 4', [50.0, 0.2, 11.0, 2.0, 1.96], [-78.0, -84.5965, -68.5785])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 5', [2.0, 1.0, 10.0, 0.3, 1.645], [400.0, 119.3413, 1039.7763]),\n  ('summary sample 6', [2.0, 0.2, 55.0, 2.0, 1.96], [2650.0, 2132.3268, 3287.7209]),\n  ('summary sample 7', [50.0, 1.0, 10.0, 0.3, 1.645], [-80.0, -81.1517, -78.7779]),\n  ('summary sample 8', [2.0, 0.2, 55.0, 2.0, 1.96], [2650.0, 2132.3268, 3287.7209])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 11', [2.0, 0.5, 10.0, 0.5, 1.645], [400.0, 228.7224, 660.52]),\n  ('summary sample 12', [2.0, 1.0, 10.0, 0.5, 1.645], [400.0, 118.7667, 1042.7699]),\n  ('summary sample 13', [10.0, 1.0, 55.0, 0.3, 1.645], [450.0, 366.4607, 548.5005]),\n  ('summary sample 14', [50.0, 0.2, 11.0, 0.3, 1.645], [-78.0, -78.9753, -76.9795])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 16', [50.0, 0.2, 10.0, 0.3, 1.645], [-80.0, -80.9714, -78.9791]),\n  ('summary sample 17', [50.0, 0.2, 10.0, 0.5, 1.96], [-80.0, -81.8727, -77.9338]),\n  ('summary sample 19', [2.0, 1.0, 10.0, 0.3, 1.96], [400.0, 87.3251, 1234.5781]),\n  ('summary sample 21', [50.0, 0.2, 1.8, 0.3, 1.645], [-96.4, -97.2635, -95.2641])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 21', [50.0, 0.2, 1.8, 0.3, 1.645], [-96.4, -97.2635, -95.2641]),\n  ('summary sample 22', [10.0, 0.5, 55.0, 2.0, 1.96], [450.0, 387.2326, 520.8534]),\n  ('summary sample 26', [50.0, 0.2, 55.0, 0.5, 1.96], [10.0, 7.8794, 12.1623]),\n  ('summary sample 28', [10.0, 0.5, 1.8, 0.3, 1.96], [-82.0, -87.2016, -74.6844])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"9abf33d7c40649ea1c53f39144b08d3aa0b57f3f0158aa7bd69061f14089d564","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(mean_c, se_c, mean_t, se_t, z):\n    if mean_c <= 0 or mean_t <= 0:\n        return None\n    L = math.log(mean_t / mean_c)\n    se = math.sqrt((se_t ** 2 + se_c ** 2) / mean_c ** 2)\n    return [round(100 * (math.exp(L) - 1), 4), round(100 * (math.exp(L - z * se) - 1), 4), round(100 * (math.exp(L + z * se) - 1), 4)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 1', [10.0, 0.2, 10.0, 2.0, 1.645], [0.0, -28.1537, 39.186]),\n  ('summary sample 2', [10.0, 1.0, 1.8, 0.3, 1.645], [-82.0, -86.9258, -75.2184]),\n  ('summary sample 3', [2.0, 0.5, 1.8, 0.5, 1.96], [-10.0, -56.7354, 87.2201]),\n  ('summary sample 4', [50.0, 0.2, 11.0, 2.0, 1.96], [-78.0, -84.5965, -68.5785])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 5', [2.0, 1.0, 10.0, 0.3, 1.645], [400.0, 119.3413, 1039.7763]),\n  ('summary sample 6', [2.0, 0.2, 55.0, 2.0, 1.96], [2650.0, 2132.3268, 3287.7209]),\n  ('summary sample 7', [50.0, 1.0, 10.0, 0.3, 1.645], [-80.0, -81.1517, -78.7779]),\n  ('summary sample 8', [2.0, 0.2, 55.0, 2.0, 1.96], [2650.0, 2132.3268, 3287.7209])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 11', [2.0, 0.5, 10.0, 0.5, 1.645], [400.0, 228.7224, 660.52]),\n  ('summary sample 12', [2.0, 1.0, 10.0, 0.5, 1.645], [400.0, 118.7667, 1042.7699]),\n  ('summary sample 13', [10.0, 1.0, 55.0, 0.3, 1.645], [450.0, 366.4607, 548.5005]),\n  ('summary sample 14', [50.0, 0.2, 11.0, 0.3, 1.645], [-78.0, -78.9753, -76.9795])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 16', [50.0, 0.2, 10.0, 0.3, 1.645], [-80.0, -80.9714, -78.9791]),\n  ('summary sample 17', [50.0, 0.2, 10.0, 0.5, 1.96], [-80.0, -81.8727, -77.9338]),\n  ('summary sample 19', [2.0, 1.0, 10.0, 0.3, 1.96], [400.0, 87.3251, 1234.5781]),\n  ('summary sample 21', [50.0, 0.2, 1.8, 0.3, 1.645], [-96.4, -97.2635, -95.2641])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 21', [50.0, 0.2, 1.8, 0.3, 1.645], [-96.4, -97.2635, -95.2641]),\n  ('summary sample 22', [10.0, 0.5, 55.0, 2.0, 1.96], [450.0, 387.2326, 520.8534]),\n  ('summary sample 26', [50.0, 0.2, 55.0, 0.5, 1.96], [10.0, 7.8794, 12.1623]),\n  ('summary sample 28', [10.0, 0.5, 1.8, 0.3, 1.96], [-82.0, -87.2016, -74.6844])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"0accc0eb5c94ea8e1625ccb55708c20ae4742bbeb95f63d75fecd37a0abaca7e","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(mean_c, se_c, mean_t, se_t, z):\n    if mean_c <= 0 or mean_t <= 0:\n        return None\n    L = math.log(mean_t / mean_c)\n    se = math.sqrt((se_t / mean_t) ** 2 + (se_c / mean_c) ** 2)\n    return [round(100 * (math.exp(L) - 1), 4), round(100 * (math.exp(L - z * se) - 1), 4), round(100 * (math.exp(L + z * se) - 1), 4)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 1', [10.0, 0.2, 10.0, 2.0, 1.645], [0.0, -28.1537, 39.186]),\n  ('summary sample 2', [10.0, 1.0, 1.8, 0.3, 1.645], [-82.0, -86.9258, -75.2184]),\n  ('summary sample 3', [2.0, 0.5, 1.8, 0.5, 1.96], [-10.0, -56.7354, 87.2201]),\n  ('summary sample 4', [50.0, 0.2, 11.0, 2.0, 1.96], [-78.0, -84.5965, -68.5785])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 5', [2.0, 1.0, 10.0, 0.3, 1.645], [400.0, 119.3413, 1039.7763]),\n  ('summary sample 6', [2.0, 0.2, 55.0, 2.0, 1.96], [2650.0, 2132.3268, 3287.7209]),\n  ('summary sample 7', [50.0, 1.0, 10.0, 0.3, 1.645], [-80.0, -81.1517, -78.7779]),\n  ('summary sample 8', [2.0, 0.2, 55.0, 2.0, 1.96], [2650.0, 2132.3268, 3287.7209])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 11', [2.0, 0.5, 10.0, 0.5, 1.645], [400.0, 228.7224, 660.52]),\n  ('summary sample 12', [2.0, 1.0, 10.0, 0.5, 1.645], [400.0, 118.7667, 1042.7699]),\n  ('summary sample 13', [10.0, 1.0, 55.0, 0.3, 1.645], [450.0, 366.4607, 548.5005]),\n  ('summary sample 14', [50.0, 0.2, 11.0, 0.3, 1.645], [-78.0, -78.9753, -76.9795])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 16', [50.0, 0.2, 10.0, 0.3, 1.645], [-80.0, -80.9714, -78.9791]),\n  ('summary sample 17', [50.0, 0.2, 10.0, 0.5, 1.96], [-80.0, -81.8727, -77.9338]),\n  ('summary sample 19', [2.0, 1.0, 10.0, 0.3, 1.96], [400.0, 87.3251, 1234.5781]),\n  ('summary sample 21', [50.0, 0.2, 1.8, 0.3, 1.645], [-96.4, -97.2635, -95.2641])],\n [('ten percent increase', [10.0, 0.2, 11.0, 0.3, 1.96], [10.0, 2.9448, 17.5387]),\n  ('decrease', [2.0, 0.5, 1.8, 0.5, 1.645], [-10.0, -51.3304, 66.4285]),\n  ('no change', [50.0, 1.0, 50.0, 2.0, 1.96], [0.0, -8.3922, 9.161]),\n  ('zero control mean', [0.0, 1.0, 2.0, 1.0, 1.96], None),\n  ('summary sample 21', [50.0, 0.2, 1.8, 0.3, 1.645], [-96.4, -97.2635, -95.2641]),\n  ('summary sample 22', [10.0, 0.5, 55.0, 2.0, 1.96], [450.0, 387.2326, 520.8534]),\n  ('summary sample 26', [50.0, 0.2, 55.0, 0.5, 1.96], [10.0, 7.8794, 12.1623]),\n  ('summary sample 28', [10.0, 0.5, 1.8, 0.3, 1.96], [-82.0, -87.2016, -74.6844])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic toy experiment-analysis model with a stipulated contract; results are rounded and are not a substitute for a validated statistics package. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-experiment-statistics-log-ratio-interval-relative-standard-errors","generated_at":"2026-09-29T14:49:00.317436+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Log-ratio intervals keep percent-change bounds asymmetric and positive-definite.","repair":"Scale each standard error by its own arm mean before combining.","root_cause":"The log-scale variance is (se_t^2 + se_c^2) / mean_c^2.","sha256":"77b0b0d6746960702f71432e9833b07714c18cebd476b438ddff864f1bfa1519","title":"Percent change log-ratio interval: Standard errors are scaled by the control mean only · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":38.264,"exit_code":1,"observations":[{"actual":[10.0,0.2659,20.6791],"check":"ten percent increase","expected":[10.0,2.9448,17.5387],"passed":false},{"actual":[-10.0,-62.2262,114.4345],"check":"decrease","expected":[-10.0,-51.3304,66.4285],"passed":false},{"actual":[0.0,-11.0948,12.4794],"check":"no change","expected":[0.0,-8.3922,9.161],"passed":false},{"actual":null,"check":"zero control mean","expected":null,"passed":true},{"actual":[0.0,-30.3648,43.6055],"check":"summary sample 1","expected":[0.0,-28.1537,39.186],"passed":false},{"actual":[-82.0,-88.3919,-72.0885],"check":"summary sample 2","expected":[-82.0,-86.9258,-75.2184],"passed":false},{"actual":[-10.0,-68.0119,153.2188],"check":"summary sample 3","expected":[-10.0,-56.7354,87.2201],"passed":false},{"actual":[-78.0,-84.7155,-68.3339],"check":"summary sample 4","expected":[-78.0,-84.5965,-68.5785],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"ten percent increase\", \"actual\": [10.0, 0.2659, 20.6791], \"expected\": [10.0, 2.9448, 17.5387], \"passed\": false}, {\"check\": \"decrease\", \"actual\": [-10.0, -62.2262, 114.4345], \"expected\": [-10.0, -51.3304, 66.4285], \"passed\": false}, {\"check\": \"no change\", \"actual\": [0.0, -11.0948, 12.4794], \"expected\": [0.0, -8.3922, 9.161], \"passed\": false}, {\"check\": \"zero control mean\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"summary sample 1\", \"actual\": [0.0, -30.3648, 43.6055], \"expected\": [0.0, -28.1537, 39.186], \"passed\": false}, {\"check\": \"summary sample 2\", \"actual\": [-82.0, -88.3919, -72.0885], \"expected\": [-82.0, -86.9258, -75.2184], \"passed\": false}, {\"check\": \"summary sample 3\", \"actual\": [-10.0, -68.0119, 153.2188], \"expected\": [-10.0, -56.7354, 87.2201], \"passed\": false}, {\"check\": \"summary sample 4\", \"actual\": [-78.0, -84.7155, -68.3339], \"expected\": [-78.0, -84.5965, -68.5785], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.297,"exit_code":1,"observations":[{"actual":[10.0,2.4947,18.0548],"check":"ten percent increase","expected":[10.0,2.9448,17.5387],"passed":false},{"actual":[-10.0,-49.6895,61.0001],"check":"decrease","expected":[-10.0,-51.3304,66.4285],"passed":false},{"actual":[0.0,-8.3922,9.161],"check":"no change","expected":[0.0,-8.3922,9.161],"passed":true},{"actual":null,"check":"zero control mean","expected":null,"passed":true},{"actual":[0.0,-28.1537,39.186],"check":"summary sample 1","expected":[0.0,-28.1537,39.186],"passed":true},{"actual":[-82.0,-84.8405,-78.6273],"check":"summary sample 2","expected":[-82.0,-86.9258,-75.2184],"passed":false},{"actual":[-10.0,-54.9918,79.9671],"check":"summary sample 3","expected":[-10.0,-56.7354,87.2201],"passed":false},{"actual":[-78.0,-79.6669,-76.1965],"check":"summary 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