Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, \(n\approx 25\) and coefficients of variation \(\approx 0.30\). The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to 0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences in the confidence limits were small from a clinical or managerial perspective, with a maximum absolute difference in ratios of 0.016. Thus, P < 0.05 is reliable, but investigators should plan for Type II errors at greater than nominal rates.