Many computational approaches have been developed and used for sampling protein conformations near the native state. However, it has been difficult to evaluate the quality of the conformations sampled or to compare them among the various sampling schemes. In this work, we develop a novel method for evaluating the quality of conformation ensembles and apply it to evaluate ubiquitin conformations generated from four widely-used conformation sampling approaches, namely, MD simulation, Elastic Network Model (ENM), CONCOORD, and tCONCOORD. We choose ubiquitin because there exists abundant experimental residual dipolar coupling (RDC) data on this protein. RDC data contains rich ensemble-averaged information about a given protein and thus provide tight constraints that can be used for probing what conformations should make up the protein ensemble. Our results demonstrate that the conformations generated by MD simulations are the best among all sampling methods. Specifically, MD simulation performs significantly better than the other methods in capturing the side chain motions. The backbone flexibility modeled and sampled by tCONCOORD comes quite close, with CONCO-ORD and ENM trailing behind.