  A range of Bayesian tools has become widely used in cosmological data
treatment and parameter inference (see Kunz, Bassett & Hlozek (2007), Trotta
(2008), Amendola, Marra & Quartin (2013)). With increasingly big datasets and
higher precision, tools that enable us to further enhance the accuracy of our
measurements gain importance. Here we present an approach based on internal
robustness, introduced in Amendola, Marra & Quartin (2013) and adopted in
Heneka, Marra & Amendola (2014), to identify biased subsets of data and hidden
correlation in a model independent way.
