The use of electronic structure calculations to rational, guide and support experiments has become a routine in different branches of science. Such practical calculations, be they based on wave-function or density functional theory (DFT) approximations, conflate errors in both approximate molecular geometries and total energies. Thus, a proper analysis of the performance of electronic structure methods requires decoupling these two distinct sources of errors and their separate analysis. The energetic performance of electronic structure methods is often drastically different from their performance for molecular geometries. Methods with comparable energetic performance, for e.g., binding energies of noncovalent systems, can have strikingly different geometric performance for the same systems. Other examples include Hartree-Fock (HF) and the local density approximation (LDA), which give reasonable structures despite their poor energetic performance. Nearly all electronic structure simulations (and beyond) begin with obtaining approximate geometries, making a proper and extensive quantification of geometric errors of key importance in computational chemistry. By using the standard tools for comparing structures, it is not easy to tell which of approximate geometries is better, as it requires comparing errors in 3N -6 degrees of freedom. What typically happens is that some geometric parameters are more accurate in one method, some are better in another (see, e.g., Refs. 1-3). The geometric performance of electronic structure methods is commonly assessed by comparing averages of errors in these parameters (e.g., bond lengths, angles, distances from a chosen point in a molecule such as the center of mass, etc.) But, when such metrics are used, the rankings of approximations strongly depends on a chosen metric as illustrated in detail in Ref. 1. By comparing errors in geometric parameters, it is not trivial to tell which of the approximations yields an overall better geometry even for systems with two degrees of freedom (e.g., water ), as one approximation can beat the other for the first degree of freedom, but not for the second. Other ambiguities that affect the rankings based on this approach also arise, such as: whether one should average errors over all bond lengths/angles or only unique ones.