A key issue in science is assessing robustness to data analysis choices, while avoiding selective reporting and providing valid inference. Specification Curve Analysis is a tool intended to prevent selective reporting. Alas, when used for inference it can create severe biases and false positives, due to wrongly adjusting for covariates, and mask important treatment effect heterogeneity. As our motivating application, it led an influential study to conclude there is no relevant association between technology use and teenager mental well-being. We discuss these issues and propose a strategy for valid inference. Bayesian Specification Curve Analysis (BSCA) uses Bayesian Model Averaging to incorporate covariates and heterogeneous effects across treatments, outcomes and subpopulations. BSCA gives significantly different insights into teenager well-being, revealing that the association with technology differs by device, gender and who assesses well-being (teenagers or their parents).

中文翻译：

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技术使用和青少年幸福感的规范分析：统计有效性和贝叶斯建议

科学中的一个关键问题是评估数据分析选择的稳健性，同时避免选择性报告和提供有效推论。规格曲线分析是一种旨在防止选择性报告的工具。las，当用于推理时，由于对协变量的错误调整，它会产生严重的偏差和误报，并掩盖重要的治疗效果异质性。作为我们的激励应用，它导致了一项有影响力的研究得出结论，技术使用与青少年心理健康之间没有相关关联。我们讨论这些问题并提出有效推理的策略。贝叶斯规格曲线分析 (BSCA) 使用贝叶斯模型平均来整合治疗、结果和亚群之间的协变量和异质效应。