undervalidation

Undervalidation is a term used to describe the situation in which the validation of a method, model, diagnostic test, or measurement is insufficient to demonstrate its reliability, accuracy, or generalizability. It can apply across fields such as data science, engineering, and clinical research. In practice, undervalidation means that the available validation evidence does not adequately support performance in real-world or diverse settings.

Common causes include reliance on small or non-representative validation data, the absence of external or prospective validation, and validation conducted only on retrospective or biased samples. Other contributing factors are inadequate calibration assessment, improper handling of data splits or leakage, and preregistration or reporting gaps that prevent independent verification of results. Since validation quality varies by context, what constitutes sufficient validation is not always uniform.

The consequences of undervalidation can be substantial. Conclusions based on insufficient validation may overstate accuracy, fail to generalize to new populations or conditions, and lead to poor decision-making, reduced reproducibility, or safety risks in clinical or regulatory contexts. In fields with high stakes, undervalidation can erode trust in the results and impede adoption of useful tools.

Mitigation involves robust validation practices: using external or prospective validation cohorts, performing calibration analyses, ensuring adequate and representative sample sizes, preregistering validation plans, and following transparent reporting and replication standards. While the phrase undervalidation is not a universally formal term, it highlights the need for thorough, independent evidence before applying methods beyond their initial development context.