Lists of invasive alien species (IAS) are essential for preventing, controlling, and reporting on the state of biological invasions. However, these lists suffer from a range of errors, with serious consequences for their use in science, policy, and management. Here we (1) collated and classified errors in IAS listing using a taxonomy of uncertainty; and (2) estimated the size of these errors using data from a completed listing exercise, with the purpose of better understanding, communicating, and dealing with them. Ten errors were identified. Most result from a lack of knowledge or measurement error (epistemic uncertainty), although two were a result of context dependence and vagueness (linguistic uncertainty). Estimates of the size of the effects of these errors were substantial in a number of cases and unknown in others. Most errors, and those with the largest estimated effect, result in underestimates of IAS numbers. However, there are a number of errors where the size and direction of the effect remains poorly understood. The effect of differences in opinion between specialists is potentially large, particularly for data-poor taxa and regions, and does not have a clearly directional or consistent effect on the size and composition of IAS lists. Five tactics emerged as important for reducing uncertainty in IAS lists, and while uncertainty will never be removed entirely, these approaches will significantly improve the transparency, repeatability, and comparability of IAS lists. Understanding the errors and uncertainties that occur during the process of listing invasive species, as well as the potential size and nature of their effects on IAS lists, is key to improving the value of these lists for governments, management agencies, and conservationists. Such understanding is increasingly important given positive trends in biological invasion and the associated risks to biodiversity and biosecurity.