{ "evaluation_id": "EVAL-20260318-163754", "question_id": "EVAL-20260318-163754", "question_text": "During WWII, analysts studied bullet holes on returning bombers to decide where to add armor. They found most damage on the wings and fuselage, almost none on the engines. Their recommendation: armor the wings. Abraham Wald disagreed. (1) What was Wald's reasoning? (2) Give 5 modern examples of survivorship bias in business/tech. (3) 'We studied 100 successful startups and found they all did X.' Why is this analysis worthless without a control group?", "category": "reasoning", "timestamp": "2026-03-18T16:37:54.681Z", "display_date": "Mar 18, 2026", "winner": { "name": "GPT-5.4", "provider": "openrouter", "score": 9.73 }, "avg_score": 9.485, "matrix_size": 56, "models_used": [ { "id": "minimax_m25", "name": "MiniMax M2.5", "provider": "openrouter" }, { "id": "minimax_m21", "name": "MiniMax M2.1", "provider": "openrouter" }, { "id": "minimax_m2", "name": "MiniMax M2", "provider": "MiniMax" }, { "id": "minimax_m1", "name": "MiniMax M1", "provider": "openrouter" }, { "id": "minimax_01", "name": "MiniMax-01", "provider": "openrouter" }, { "id": "judge_claude_sonnet", "name": "Claude Sonnet 4.6", "provider": "openrouter" }, { "id": "judge_gpt54", "name": "GPT-5.4", "provider": "openrouter" }, { "id": "minimax_m27", "name": "MiniMax M2.7", "provider": "openrouter" } ], "rankings": { "judge_gpt54": { "display_name": "GPT-5.4", "provider": "openrouter", "average_score": 9.73, "score_count": 7, "min_score": 9, "max_score": 10, "rank": 1 }, "minimax_m2": { "display_name": "MiniMax M2", "provider": "MiniMax", "average_score": 9.69, "score_count": 7, "min_score": 9, "max_score": 10, "rank": 2 }, "minimax_m27": { "display_name": "MiniMax M2.7", "provider": "openrouter", "average_score": 9.67, "score_count": 7, "min_score": 8.45, "max_score": 10, "rank": 3 }, "minimax_m1": { "display_name": "MiniMax M1", "provider": "openrouter", "average_score": 9.66, "score_count": 7, "min_score": 9, "max_score": 10, "rank": 4 }, "judge_claude_sonnet": { "display_name": "Claude Sonnet 4.6", "provider": "openrouter", "average_score": 9.61, "score_count": 7, "min_score": 8.65, "max_score": 10, "rank": 5 }, "minimax_m25": { "display_name": "MiniMax M2.5", "provider": "openrouter", "average_score": 9.4, "score_count": 7, "min_score": 8.25, "max_score": 10, "rank": 6 }, "minimax_m21": { "display_name": "MiniMax M2.1", "provider": "openrouter", "average_score": 9.29, "score_count": 7, "min_score": 7.65, "max_score": 9.85, "rank": 7 }, "minimax_01": { "display_name": "MiniMax-01", "provider": "openrouter", "average_score": 8.83, "score_count": 7, "min_score": 8.25, "max_score": 9.6, "rank": 8 } }, "judgments": [ { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.45, "brief_justification": "The response accurately explains Wald's reasoning about non-survivable hits on engines, provides 5 well-chosen modern examples of survivorship bias across different business/tech domains, and clearly explains why studying only successful startups without a control group produces meaningless conclusions. The structure is excellent with clear section headers and the explanations are logically sound." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 9.25, "brief_justification": "The response provides accurate and thorough explanations of Wald's counterfactual reasoning, offers five well-reasoned modern examples of survivorship bias, and comprehensively explains why the startup analysis is worthless without a control group. The response is cut off mid-sentence at the end, which slightly reduces completeness, but the core analysis is excellent throughout." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 10, "brief_justification": "The response provides a thorough, accurate explanation of Wald's reasoning regarding survivorship bias, offers five well-chosen modern business/tech examples, and clearly explains why startup analysis without a control group is methodologically flawed using key statistical concepts like base rate and omitted variable bias." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9.4, "brief_justification": "The response accurately explains Wald's core insight about missing data, provides five well-chosen modern examples in an organized table, and delivers a thorough explanation of why control groups are essential—correctly identifying selection on the dependent variable as the key methodological flaw. The structure is exemplary with clear headings, good use of formatting, and a logical flow." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 9, "brief_justification": "The response accurately explains Wald's survivorship bias reasoning with correct historical facts, provides five relevant modern examples with clear explanations, and thoroughly addresses why startup analysis without a control group is statistically flawed, covering multiple angles like selection bias and alternative explanations." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.4, "brief_justification": "The response is exceptionally clear and insightful, correctly explaining Wald's statistical reasoning with formal logic, providing well-analyzed modern examples with concrete data, and thoroughly demonstrating why control groups are essential. Docked one point for completeness because the response is cut off mid-sentence at the end, and one point for usefulness due to this truncation." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9, "brief_justification": "The response accurately explains Wald's survivorship bias reasoning, provides five well-chosen modern examples covering different domains (startup habits, culture, VC investing, product strategy, and remote work), and thoroughly explains why a control group is essential with a concrete numerical illustration. The structure is clear and pedagogically effective." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 10, "brief_justification": "Comprehensive and accurate response that correctly explains Wald's counterintuitive reasoning (armor where returning planes have NO damage), provides five relevant and distinct modern examples of survivorship bias, and thoroughly explains why the startup analysis is logically flawed without a control group. Well-structured with clear explanations." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 9.8, "brief_justification": "Excellent response that accurately explains Wald's insight about the 'missing' data, provides five well-reasoned modern examples, and thoroughly explains why control groups are essential. Minor deduction for the truncated ending in part 3, though the key points are made." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 10, "brief_justification": "Excellent response covering all three parts thoroughly. Correctly explains Wald's insight about missing data, provides 5 relevant modern examples with context, and gives a multi-faceted explanation of why the startup analysis fails without a control group. Well-structured with clear logic and insightful analogies." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 10, "brief_justification": "Comprehensive response that correctly explains Wald's insight about missing data, provides five distinct and relevant modern examples, and thoroughly explains the statistical flaw in studying only successful startups. Well-structured with clear explanations of key concepts like selection bias and the importance of control groups." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 9.6, "brief_justification": "The response accurately explains Wald's survivorship bias reasoning, provides 5 relevant modern examples, and thoroughly explains why the startup analysis is flawed without a control group. Well-structured and educational, though the examples could be slightly more detailed." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "Comprehensive response covering all three parts with accurate explanations, well-structured presentation, insightful analysis of mechanisms and real-world harm, and actionable methodology for proper analysis." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 10, "brief_justification": "Excellent response that correctly explains Wald's survivorship bias reasoning, provides five highly relevant modern business/tech examples, and thoroughly explains why control groups are essential with a clear illustrative example. Well-organized, insightful, and practically useful." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 10, "brief_justification": "Comprehensive and accurate response covering all three parts with clear explanations, relevant modern examples, and thorough analysis of the statistical reasoning behind control group requirements." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.6, "brief_justification": "Accurate and comprehensive explanation of survivorship bias with relevant modern examples; clearly structured and highly practical, though could have explored statistical nuances more deeply." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 9.8, "brief_justification": "The response is factually accurate and comprehensively addresses all three parts of the question. Wald's reasoning is correctly explained with the key insight about 'missing holes.' The five examples are relevant and varied. The control group explanation is thorough, covering selection bias, correlation vs causation, base rate, and omitted variable bias. Slightly deducted on depth for not exploring additional nuances like the mathematical probability aspect of Wald's work." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 10, "brief_justification": "Comprehensive, accurate explanation covering all three parts with excellent structure, insightful analysis of the missing data problem, and practical modern examples that make the concept actionable." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 9.05, "brief_justification": "Response correctly explains Wald's survivorship bias reasoning, provides relevant modern examples with minor issues (university rankings example is more selection bias than survivorship), and thoroughly explains why the startup analysis requires a control group." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.8, "brief_justification": "Excellent response that correctly explains Wald's statistical insight (survivors vs. casualties), provides five diverse and well-analyzed modern examples with concrete data, and thoroughly demonstrates why studying only successes without failure data yields meaningless conclusions. The probabilistic reasoning and base rate examples are particularly strong." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 10, "brief_justification": "Excellent response that correctly explains Wald's counterintuitive insight about armor placement, provides five relevant modern examples each with clear explanations of the bias, and thoroughly demonstrates why comparing only successes without failures (a control group) yields meaningless conclusions. Well-structured with strong examples." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 10, "brief_justification": "The response accurately explains Wald's reasoning about survivorship bias, provides exactly 5 relevant modern examples with clear explanations of the bias in each, and thoroughly explains why startup analysis without a control group is logically flawed. Well-structured and insightful throughout." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 10, "brief_justification": "The response accurately explains Wald's counterintuitive insight (armor where returning planes weren't hit), provides five diverse and relevant modern examples of survivorship bias, and thoroughly explains why startup analysis without a control group is statistically meaningless. Well-structured with strong logical flow throughout." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 9.8, "brief_justification": "Excellent response that accurately explains Wald's reasoning about missing data, provides five detailed modern examples of survivorship bias, and thoroughly explains control group importance. The response demonstrates deep statistical understanding and clear communication, though it appears slightly truncated at the end." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9.8, "brief_justification": "Excellent response that accurately explains Wald's reasoning about missing data, provides relevant modern examples, and thoroughly explains the control group requirement. Well-structured with clear formatting and practical insights." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8.8, "brief_justification": "The response accurately explains Wald's survivorship bias reasoning, provides relevant modern examples, and thoroughly explains the importance of control groups. The explanations are clear and well-structured, though could benefit from slightly more depth in the theoretical underpinnings." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.8, "brief_justification": "Excellent response with technically correct explanation of Wald's reasoning using proper conditional probability concepts. All five examples are valid and well-explained. The logical analysis of why the startup study is worthless is thorough, using proper 2x2 contingency table framework. Slight deduction for completeness as the response appears cut off at the end, but all three parts of the question are fully addressed." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9.45, "brief_justification": "Response correctly explains Wald's survivorship bias insight and provides five relevant modern examples. Explains control group necessity thoroughly with clear examples. Slightly incomplete because it doesn't explicitly state Wald's recommendation to armor the engines (though implied). Well-structured and highly instructive." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 10, "brief_justification": "Comprehensive and accurate response covering all three parts with excellent explanations. Wald's reasoning is correctly identified, examples are relevant and well-explained, and the control group explanation thoroughly demonstrates the logical fallacy." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.25, "brief_justification": "The response accurately explains Wald's reasoning about survivorship bias and correctly identifies why the startup analysis is flawed. The examples are reasonable but somewhat generic, and the depth could be improved with more specific or nuanced examples." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 9, "brief_justification": "Excellent response that accurately explains Wald's counterfactual reasoning about missing data, provides five relevant and well-explained modern examples of survivorship bias, and thoroughly addresses why control groups are essential for meaningful analysis. The response is well-structured, insightful, and practically useful, with only minor incompleteness due to the trailing incomplete sentence." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 9.6, "brief_justification": "The response provides accurate, comprehensive explanations of Wald's insight, relevant modern examples, and a thorough breakdown of why control groups are essential. Well-structured with clear reasoning, though slightly verbose." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8.85, "brief_justification": "Response is factually accurate with good structure and clear explanations. Deducts points for depth as explanations are correct but surface-level without deeper statistical insights or more nuanced analysis of the examples." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.6, "brief_justification": "Excellent response with accurate logical reasoning about Wald's insight, well-chosen modern examples with supporting data, and a thorough explanation of why success-only studies are fundamentally flawed. Slight deduction for abrupt ending in Part 3. Overall exemplary treatment of survivorship bias." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 10, "brief_justification": "Excellent response that correctly explains Wald's counter-intuitive reasoning about armoring engines (where returning planes weren't hit), provides five highly relevant modern business/tech examples of survivorship bias, and clearly explains why studying only successful startups without a control group fails to establish causation. Well-structured with strong reasoning throughout." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 9.8, "brief_justification": "The response demonstrates exceptional understanding of survivorship bias, provides accurate and thorough explanations, and includes well-chosen modern examples. The analysis is highly insightful, though it could slightly expand on the broader implications of survivorship bias in decision-making." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 10, "brief_justification": "The response provides a highly accurate, thorough, and insightful explanation of survivorship bias, including Wald's reasoning, modern examples, and the critical role of control groups in analysis." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 9.85, "brief_justification": "The response demonstrates exceptional understanding of survivorship bias, provides a comprehensive analysis of Wald's reasoning, and offers detailed modern examples. The explanation of why the startup analysis is flawed is thorough and clear, but it could include more actionable takeaways for improvement." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 10, "brief_justification": "The response demonstrates exceptional understanding of survivorship bias, provides a comprehensive explanation of Wald's reasoning, offers diverse and relevant modern examples, and thoroughly explains why control groups are crucial in analysis." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9.65, "brief_justification": "The response provides a highly accurate, comprehensive, and insightful analysis of survivorship bias, including Wald's reasoning, modern examples, and the critical role of control groups in avoiding this bias. The explanation is clear and well-structured, with only minor improvements needed in flow or emphasis for maximum clarity and practical value." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "The response provides a comprehensive, accurate, and insightful analysis of survivorship bias, with clear reasoning, numerous relevant examples, and practical guidance for avoiding common pitfalls." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9.85, "brief_justification": "The response is factually accurate, thoroughly explains the concepts, and provides clear examples of survivorship bias. It demonstrates deep understanding and offers practical insights, but the usefulness is marginally reduced by the lack of explicit actionable takeaways for each example." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 9.45, "brief_justification": "The response accurately explains Wald's reasoning, provides five relevant and well-explained examples of survivorship bias, and clearly articulates why a control group is essential. The analysis is logically rigorous, well-structured, and practically valuable, with only minor room for deeper statistical nuance." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.25, "brief_justification": "The response accurately explains Wald's reasoning, provides relevant and varied modern examples of survivorship bias, and clearly articulates why a control group is essential. The examples could be slightly more diverse or specific, but overall the answer is well-structured, logically sound, and practically valuable." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 9.65, "brief_justification": "The response is factually accurate, deeply analytical, and exceptionally well-structured, covering Wald's reasoning with precision and providing rich, varied modern examples. The only minor issue is that the response appears to be cut off mid-sentence in the final section, preventing a fully complete conclusion." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 9.45, "brief_justification": "The response is factually accurate and well-structured, correctly explaining Wald's reasoning, providing five relevant and distinct modern examples, and clearly articulating why a control group is essential. The analysis goes beyond surface-level explanation with strong analogies and multi-dimensional reasoning about base rates, causation, and omitted variables." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9.8, "brief_justification": "The response accurately explains Wald's reasoning, provides five distinct and relevant modern examples of survivorship bias, and clearly articulates why studying only successful startups is methodologically flawed. The use of tables to illustrate the control group concept is particularly effective and actionable." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8.25, "brief_justification": "The response accurately explains Wald's reasoning and survivorship bias, provides relevant modern examples, and clearly explains why a control group is necessary. The examples could be more specific and novel, and the analysis of the control group question could go deeper into base rate issues and the need to study failed startups doing X vs. not doing X." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9.8, "brief_justification": "The response is factually accurate and logically rigorous throughout, correctly explaining Wald's reasoning, providing five distinct and well-reasoned modern examples, and clearly articulating why a control group is essential. The 'Compared to what?' framing and the numerical example in section 3 add practical depth and make the abstract concept concrete." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 8.45, "brief_justification": "The response correctly explains Wald's survivorship-bias reasoning and gives a solid explanation of why a control group is necessary. It is clear and practical, though a couple of the five examples drift outside business/tech and one example is phrased awkwardly, so completeness and usefulness are slightly reduced." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.25, "brief_justification": "The response accurately explains Wald's survivorship-bias reasoning, gives five relevant modern examples, and clearly explains why a control group is necessary. It is well-structured and practical, though the examples could be slightly more analytically detailed or business-specific in places." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 7.65, "brief_justification": "The response correctly explains Wald's reasoning and gives five plausible survivorship-bias examples with solid discussion of control groups and base rates. However, it is incomplete because the final section is cut off, and one example (great teams) is a bit less concrete/business-tech specific than the others." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 9, "brief_justification": "The response correctly explains Wald’s survivorship-bias reasoning, gives five relevant modern examples, and clearly explains why a control group is necessary using base rates and correlation-vs-causation. Minor weaknesses are that a few examples are a bit loose or rhetorical rather than rigorously framed, but overall it is strong, clear, and practical." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9, "brief_justification": "The response correctly explains Wald’s reasoning, gives five relevant modern survivorship-bias examples, and clearly explains why the startup claim fails without a control group. It is well-structured and practical, though a few examples are slightly broad and one comparison (iPhones and Nokias) is less precise than ideal." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8.25, "brief_justification": "The response correctly explains Wald’s survivorship-bias reasoning and why a control group is necessary, with clear structure and generally sound examples. It loses points because a few examples are weaker or less directly business/tech-specific, and the control-group discussion could more explicitly frame base rates and causal inference." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.65, "brief_justification": "The response correctly explains Wald's reasoning, gives five relevant modern examples, and clearly shows why a control group is necessary. It is slightly incomplete because the final section is cut off and a few empirical claims are asserted without sourcing, but overall it is strong, clear, and practically useful." } ], "meta": { "source": "The Multivac (app.themultivac.com)", "methodology": "10x10 blind peer matrix evaluation", "criteria": "correctness, completeness, clarity, depth, usefulness", "self_judgments": "excluded from rankings", "license": "Open data — cite as: The Multivac (2026)" } }