Robust test statistics for data sets with missing correlation information

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 overestimation or underestimation of the resulting uncertainties. This work presents robust test statistics that can be used with datasets 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.