Agentic Bootstrap: Using AI Agents to Test Research Robustness
james_y_zou · x · 2026-07-07
A new study points out that researchers' prior beliefs can influence data analysis conclusions—the same dataset can yield opposite results for people with different stances. AI agents assigned different personas can successfully replicate these discrepancies among human analysts.
To address this, the team proposes "Agentic Bootstrap," using agents to systematically enumerate the hidden choices in the analysis process (the "garden of forking paths") to see how each choice impacts the final conclusion. They introduce the "m-value" to measure the robustness of a conclusion: a high m-value means the conclusion doesn't depend on a specific analytical path, while a cherry-picked conclusion has a low m-value. The m-value is orthogonal and complementary to the traditional p-value. The paper and code have been open-sourced.
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