In an effort to improve the probability of correctly associating tracks and observations, features, which are physical properties of the target other than kinematics, are included in the association process. Unlike their kinematic counter parts, the probability distributions of the features are typically not known for all of the objects involved. This precludes the use of the parametric hypothesis tests typically used with the kinematic data to perform association. One possible solution to this problem is to assume a probability distribution for each of the features and use it in the conventional parametric test used for association. The risk is that the wrong probability distributions will be assumed and the association error probability will increase. An alternative approach is to use the feature data in a non-parametric test, a type of test that requires little or no knowledge of the probability distribution of the data. The result of the non-parametric test of the feature data is then combined with the result of the conventional parametric test of the kinematic data. As the title suggests, this paper compares the performance of these two approaches for several sets of conditions. First, since the parametric test of the kinematic data assumes the data to be Gaussian distributed, the features are drawn initially from Gaussian populations. These Gaussian distributed features are used to test both approaches and their performance curves are compared and analyzed. This process is then repeated for three non-Gaussian feature distributions. Two of these distributions belong to the same exponential family of distributions as the Gaussian Distribution however, both have heavier tails and one is a one-sided distribution. The third feature distribution used in the comparison has finite support and the experiment is designed so that perfect performance is possible. Realizing that the association error probability is not zero, the foregoing evaluations are repeated with misassociations present in the data.