The item count technique (ICT-MLE) regression model for survey list experiments depends on assumptions about responses at the extremes (choosing no or all items on the list). Existing list experiment best practices aim to minimize strategic misrepresentation in ways that virtually guarantee that a tiny number of respondents appear in the extrema. Under such conditions both the “no liars” identification assumption and the computational strategy used to estimate the ICT-MLE become difficult to sustain. I report the results of Monte Carlo experiments examining the sensitivity of the ICT-MLE and simple difference-in-means estimators to survey design choices and small amounts of non-strategic respondent error. I show that, compared to the difference in means, the performance of the ICT-MLE depends on list design. Both estimators are sensitive to measurement error, but the problems are more severe for the ICT-MLE as a direct consequence of the no liars assumption. These problems become extreme as the number of treatment-group respondents choosing all the items on the list decreases. I document that such problems can arise in real-world applications, provide guidance for applied work, and suggest directions for further research.