When synthetic data are produced to overcome potential disclosure they can be used either in place of the original data or, more commonly, to allow researchers to develop code that will ultimately be run on the original data. The utility of synthetic data can be measured by comparing the results of the final analysis with the synthetic and original data. This is not possible until the final analysis is complete. General utility measures that measure the overall differences between the original and synthetic data are more useful for those creating synthetic data. This presentation will discuss two such measures. The first is a propensity score measure originally proposed by Woo et. al., 2009 and the second is one based on comparing tables, suggested by Voas and Williamson, 2001. Their null distributions, when the synthesis model is "correct" will be discussed as well as their practical implementation as part of the synthpop package.