Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911564958793728 |
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| author | Thiel, Konstantin Emil Baumeister, Marléne Krämer, Nicole Groll, Andreas Pauly, Markus Wischnewski, Magdalena |
| author_facet | Thiel, Konstantin Emil Baumeister, Marléne Krämer, Nicole Groll, Andreas Pauly, Markus Wischnewski, Magdalena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02992 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods Thiel, Konstantin Emil Baumeister, Marléne Krämer, Nicole Groll, Andreas Pauly, Markus Wischnewski, Magdalena Other Statistics Applications Methodology 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. |
| title | Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods |
| topic | Other Statistics Applications Methodology |
| url | https://arxiv.org/abs/2604.02992 |