  We extend the use of Classification Without Labels for anomaly detection with
a hypothesis test designed to exclude the background-only hypothesis. By
testing for statistical independence of the two discriminating dataset regions,
we are able to exclude the background-only hypothesis without relying on fixed
anomaly score cuts or extrapolations of background estimates between regions.
The method relies on the assumption of conditional independence of anomaly
score features and dataset regions, which can be ensured using existing
decorrelation techniques. As a benchmark example, we consider the LHC Olympics
dataset where we show that mutual information represents a suitable test for
statistical independence and our method exhibits excellent and robust
performance at different signal fractions even in presence of realistic feature
correlations.
