Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing

Reliable hypothesis testing is the foundation of many empirical scientific claims. Large language model (LLM) agents are increasingly used to automate this process, as they can inspect datasets, generate code, and produce analyses end-to-end. However, we show that they frequently make subtle inferential errors that lead to incorrect conclusions despite correctly executed analyses. Existing benchmarks fail to capture this failure mode, as they rarely assess whether a reported p-value is statistic

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ethics.ai (7 August 2026), “Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing,” evidence record 17914, https://ethics.ai/record/17914 (originally published by arXiv cs.AI).

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