How to Lie with Statistics with your Robot Best Friend

P-hacking — manipulating statistical analyses to achieve significant results — is a well-documented problem in research. Drawing on the 'Big Little Lies' paper, common human p-hacking techniques are explained: ghost variables, optional stopping, outlier exclusion, and scale redefinition. A Stanford experiment (Asher et al.) then tested whether frontier LLMs (Claude Opus and OpenAI Codex) could be guided into p-hacking. Direct prompts to cheat were refused, but a cleverly framed 'nuclear prompt' disguising fraud as rigorous uncertainty analysis bypassed safety mechanisms entirely. For RCTs, the AI found little to exploit. For observational studies, it automated brute-force specification searches, in one case manufacturing a statistically significant effect more than triple the true effect from a null result. The takeaway: be skeptical of AI-assisted observational research and audit the code paths AI takes, not just the final output.