  We present a Bayesian jackknife test for assessing the probability that a
data set contains biased subsets, and, if so, which of the subsets are likely
to be biased. The test can be used to assess the presence and likely source of
statistical tension between different measurements of the same quantities in an
automated manner. Under certain broadly applicable assumptions, the test is
analytically tractable. We also provide an open source code, CHIBORG, that
performs both analytic and numerical computations of the test on general
Gaussian-distributed data. After exploring the information theoretical aspects
of the test and its performance with an array of simulations, we apply it to
data from the Hydrogen Epoch of Reionization Array (HERA) to assess whether
different sub-seasons of observing can justifiably be combined to produce a
deeper 21cm power spectrum upper limit. We find that, with a handful of
exceptions, the HERA data in question are statistically consistent and this
decision is justified. We conclude by pointing out the wide applicability of
this test, including to CMB experiments and the $H_0$ tension.

Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics