  Statistical tests for dataset shift are susceptible to false alarms: they are
sensitive to minor differences when there is in fact adequate sample coverage
and predictive performance. We propose instead a framework to detect adverse
dataset shifts based on outlier scores, $\texttt{D-SOS}$ for short.
$\texttt{D-SOS}$ holds that the new (test) sample is not substantively worse
than the reference (training) sample, and not that the two are equal. The key
idea is to reduce observations to outlier scores and compare contamination
rates at varying weighted thresholds. Users can define what $\it{worse}$ means
in terms of relevant notions of outlyingness, including proxies for predictive
performance. Compared to tests of equal distribution, our approach is uniquely
tailored to serve as a robust metric for model monitoring and data validation.
We show how versatile and practical $\texttt{D-SOS}$ is on a wide range of real
and simulated data.
