A significant uncertainty in assessments of the role of clouds in climate is characterization of the full distribution of their sizes. Order-of-magnitude disagreements exist among observations of such key distribution parameters as the power law exponent and the range over which a power law applies. A study by Savre and Craig (2023) proposed this discrepancy owes in large part to inaccurate fitting methods. Rather than linear regression to a logarithmically-transformed histogram of cloud sizes, an alternative method termed Maximum Likelihood Estimation was recommended. Here, we counter that Maximum Likelihood Estimation is ill-suited to measurements of physical objects like clouds, and that the accuracy of linear regression can be improved with the simple remedy that bins containing less than ~24 counts be omitted from the regression. Further, we argue that the unavoidably finite nature of measurement domains is a much more significant source of error than has previously been appreciated. Finite domain effects are sufficient to account for previously observed discrepancies among reported cloud size distributions. We provide a simple procedure to identify and correct finite domain effects that could be applied to any measurement of a geometric size distribution of objects, whether physical, ecological, social or mathematical.