Many and multiform are the experiments that have been conducted to allow extraction of injury risk functions. In particular, human surrogate impact test results are commonly mapped into injury risk curves for the purpose of characterizing the stimulus injury response over a given range. However, it is not always clear that the quality and quantity of the data along with the experimental design are sufficient to reliably determine the desired outcome. Therefore it is desirable to obtain some form of guidance, or heuristic rules, as to the usability and appropriateness of injury risk curves with respect to sample size, stimulus distribution over the critical range, censoring, shape of the underlying risk function, and the inclusion of “actual” (uncensored) along with censored data. To accomplish this goal the Consistency threshold and Extended Consistency threshold methods along with Monte Carlo simulation are used to evaluate the experimental design of human surrogate testing. The results imply that the total amount of tests needed to generate a risk curve with a given confidence bound is dependent on the shape of the risk function along with the stimulus distribution over the critical range. This dependence can also be a function of the relative contribution of censored and actual data. However, the results from this analysis also indicate that for “large” biomechanical injury data sets there is no advantage to using actual data;​ censored data will yield the same injury risk curve as actual data. Therefore, for “small” biomechanical injury data sets the inclusion of actual data will significantly improve the quality of the resulting risk curve but not for large data sets. Confidence intervals are presented for the thoracic injury risk and the head injury risk to show the influence of data distribution on the goodness of the risk function estimation.