Systematic Errors and Intrinsic Scatter

Abstract

Certain types of uncertainty are difficult to estimate and may not be accounted in the initial error budget. This sometimes leads to a poor goodness-of-fit statistic and the rejection of the model used to fit the data. These missing sources of uncertainty may either be associated with the data themselves or with the model used to describe the data. In both cases, we describe methods to account for these errors and ensure that hypothesis testing is not biased by them.