Reporting Under Pressure: Separating Factual and Tonal Sycophancy in LLM Statistical Analysis· 压力下的报告: 大模型统计分析中的事实和语气奴性分离

Large language models are increasingly asked to analyze data and report what the results mean, a task distinct from the belief- or preference-alignment settings studied in most sycophancy research. We test whether editorial framing in the prompt, ranging from a neutral request to an explicit instruction to search exhaustively for reasons to discredit or to support a finding, changes not just the tone but the substance of a model's report. Across a 4 x 4 factorial design crossing four framing conditions with four ground-truth data patterns (a genuine effect, a confound that mimics an effect but fails a robustness check, a well-powered null, and an underpowered null), we collect 480 responses and score each along two independent dimensions: whether its factual claim about the data diverged f