Statistical practice does not automatically follow methodological innovation. Regularization methods, widely advocated to reduce overfitting and stabilize inference, are readily available in modern software, but are not consistently used by data analysts. We investigate this implementation gap in a large-scale empirical study of trust in, and acceptance of, regularization techniques, based on $N = 606$ data analysts. Drawing on measurement frameworks from technology acceptance research, we survey practitioners and embed a randomized experiment to test whether written recommendation of regularization methods increases trust or intended use. We find no evidence of such an effect. Instead, adoption intentions are strongly associated with analysts' perceptions of ease of implementation and practical benefit, such as improved bias control or interpretability. Perceived social norms also emerge as a central driver. These results indicate that uptake of statistical methodology depends less on formal recommendations than on usability, perceived utility, and community practice.

翻译：统计实践并不自动跟随方法论的创新。正则化方法被广泛推荐用于减少过拟合和稳定推断，在现代软件中已易于实现，但数据分析师并未一致使用它们。我们基于 $N = 606$ 名数据分析师，通过一项大规模实证研究，调查了对正则化技术的信任与接受度。借鉴技术接受研究中的测量框架，我们对从业者进行问卷调查，并嵌入一项随机实验，以检验正则化方法的书面推荐是否增加信任或预期使用。我们未发现此类效应的证据。相反，采纳意愿与分析者对实施便捷性和实际效益（如偏差控制或可解释性的改进）的感知密切相关。感知的社会规范也作为一个核心驱动力出现。这些结果表明，统计方法的采纳更多地依赖于可用性、感知效用和社区实践，而非正式的推荐。