Figure shows the differences between the (relatively) sparse CEEIS data and three choices of guiding potential. As before, the differences have weaker structure than the raw data. However, structure here is more noticeable than in the previous examples. For each of the three guiding potentials as g(R), we apply the four splining methods used in Table as fM(R). All results are listed in the Supporting Information. The variabilities and the results from the cubic spline are shown in Table . The most consistent results, with little dependence upon the choice of splining method, are obtained when guided by the icMRCI+Q potential. However, all the guiding potentials lead to marked improvement over the unguided results in Table , and the results from the different guiding potentials (Table ) are in mutual agreement. The closest agreement across guiding potentials, to all digits displayed in Table , is obtained from the cubic spline. Tables and consider the uncertainty arising from the choice of splining procedure, and show that it is significantly reduced by using guided fitting, instead of direct fitting with eq. ( ). One may also consider uncertainty arising from the data themselves. In this case, the CEEIS energies do not carry statistical uncertainties, as in QMC calculations. However, the selection of data points (i.e., values of R) affects the results. For example, if additional data were collected, the results would be somewhat different. This effect can be estimated from the existing data by subsampling. We take a random subset of 41 of the 43 points from Boschen et al. and analyze them using a cubic spline and either eq. (1) or eq. . Repeating this 500 times, the resulting standard deviation for the fundamental frequency is 0.79 cm -1 from direct fitting and 0.51, 0.52 and 0.52 cm -1 using the CASSCF, MRCI+Q and FCI guiding potentials, respectively. Standard deviations for the other vibrational quantities of Table are listed in the Supporting Information. Except for the experimentally unobservable ground-state energy, the standard deviations are all less than 1 cm -1 , suggesting that Boschen's dataset is large enough.