  Not all experiments publish their results with a description of the
correlations between the data points. This makes it difficult to do hypothesis
tests or model fits with that data, since just assuming no correlation can lead
to an over- or underestimation of the resulting uncertainties. This work
presents robust test statistics that can be used with data sets with missing
correlation information. They are exact in the case of no correlation and
either guaranteed to be conservative -- i.e. the uncertainty is never
underestimated -- in the presence of correlations, or they are also exact in
the degenerate case of perfect correlation between the data points.
