In order to conduct analyses of networked systems where connections between individuals take on a range of values - counts, continuous strengths or ordinal rankings - a common technique is to dichotomize the data according to their positions with respect to a threshold value. However, there are two issues to consider: how the results of the analysis depend on the choice of threshold, and what role the presence of noise has on a system with respect to a fixed threshold value. We show that while there are principled criteria of keeping information from the valued graph in the dichotomized version, they produce such a wide range of binary graphs that only a fraction of the relevant information will be kept. Additionally, while dichotomization of predictors in linear models has a known asymptotic efficiency loss, the same process applied to network edges in a time series model will lead to an efficiency loss that grows larger as the network increases in size.