用大模型生成模拟数据时，分析选择不同会导致结果差异巨大。

Social scientists are now using large language models to create "silicon samples": synthetic datasets intended to stand in for human respondents. However, producing these samples requires many analytic choices, including model selection, sampling parameters, prompt format, and the amount of demographic or contextual information provided. Across two studies, I examine whether these choices materially affect correspondence between silicon samples and human data. In Study 1, I generated 252 silicon-sample configurations for a controlled case study using two social-psychological scales, evaluating whether configurations recovered participant rankings, response distributions, and between-scale correlations. Configurations varied substantially across all three criteria, and configurations that performed well on one dimension often performed poorly on another. In Study 2, I extended this analysis to a published silicon-sample use case by re-examining Argyle et al.'s (2023) Study 3 using 66 alternative configurations. Correlations between human and silicon association structures differed substantially across configurations, from r = .23 to r = .84. Taken together, the results from these studies demonstrate that different defensible configuration choices can materially alter conclusions about the fidelity of silicon samples. I call for greater attention to the threat of analytic flexibility in using silicon samples and outline strategies that researchers may adopt to reduce this threat.