Minimum surgical times are positive and often large. The lognormal distribution has been proposed for modeling surgical data, and the three-parameter form of the lognormal, which includes a location parameter, should be appropriate for surgical data. We studied the goodness-of-fit performance, as measured by the Shapiro–Wilk p-value, of three estimators of the location parameter for the lognormal distribution, using a large data set of surgical times. Alternative models considered included the normal distribution and the two-parameter lognormal model, which sets the location parameter to zero. At least for samples with n > 30, data adequately fit by the normal had significantly smaller skewness than data not well fit by the normal, and data with larger relative minima (smallest order statistic divided by the mean) were better fit by a lognormal model. The rule ‘If the skewness of the data is greater than 0.35, use the three-parameter lognormal with the location parameter estimate proposed by Muralidhar & Zanakis, otherwise, use the two-parameter model’ works almost as well at specifying the lognormal model as more complex guidelines formulated by linear discriminant analysis and by tree induction.
