  Uncertainty quantification is a key part of astronomy and physics; scientific
researchers attempt to model both statistical and systematic uncertainties in
their data as best as possible, often using a Bayesian framework. Decisions
might then be made on the resulting uncertainty quantification -- perhaps
whether or not to believe in a certain theory, or whether to take certain
actions. However it is well known that most statistical claims should be taken
contextually; even if certain models are excluded at a very high degree of
confidence, researchers are typically aware there may be systematics that were
not accounted for, and thus typically will require confirmation from multiple
independent sources before any novel results are truly accepted. In this paper
we compare two methods in the astronomical literature that seek to attempt to
quantify these `unknown unknowns' -- in particular attempting to produce
realistic thick tails in the posterior of parameter estimation problems, that
account for the possible existence of very large unknown effects. We test these
methods on a series of case studies, and discuss how robust these methods would
be in the presence of malicious interference with the scientific data.
