Presumably, efficient perturbation designs do not require perfect edge weights, meaning that imperfect weights are likely still useful, but how useful? Or, how random or noisy can the weights be for optimal designs to still be efficient? Upon a review of the statistical literature, statisticians take liberty in assigning weights based on perceived trust in the data. As justified by Williams , "great accuracy in the weights is not necessary." In practice, if a practitioner knows that a transformation is error-prone in their protocol, they should reduce that transformation's presence in the design. This is achieved by decreasing the weight of such a transformation. Luckily, exact correspondence between inverse variances of 𝚫 Ĝ values and the weights is not necessary . This is a fortunate conclusion since, otherwise, one might need to begin by performing a much larger set of free energy calculations to determine the precision of transformations. One could then use the inverse of the resulting statistical precisions as weights. Such a method would require a researcher to perform free energy calculations before planning a weighted perturbation map. However, prior work indicates more approximate weights may suffice; as stated in Cutler and Flanagan , "it is not necessary for a priori information to be very detailed or restrictive in order that it has a significant effect on parameter-extraction accuracy." Further, even weights that only approximately correlate with variables that reduce transformation error can still dramatically improve the design performance .