Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods

TL;DR

Despite the availability of regularization methods in software, their adoption by data analysts remains low, primarily influenced by usability, perceived utility, and social norms rather than formal recommendations.

Contribution

This study empirically investigates the underuse of regularization techniques, highlighting the importance of usability and community practices over formal endorsements.

Findings

Written recommendations do not significantly increase trust or intended use.

Perceived ease of implementation strongly correlates with adoption intentions.

Social norms are a key factor influencing the uptake of regularization methods.

Abstract

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.