Recent advancements in IPv6 address discovery methods provide new capabilities for Internet measurements. However, these measurement techniques are encountering a significant challenge unique to IPv6: large IPv6 prefixes that appear responsive on all addresses. The sheer sizes of these so-called IPv6 aliases preclude each responsive address as representing distinct devices; thus, these prefixes can confound measurements of IPv6 hosts. Although prior work proposed initial methods for identifying aliased regions, there has been limited characterization of IPv6 aliases and investigation into the resulting impact on the alias detection methods. In this work, we explore IPv6 aliasing in-depth, characterizing the properties of IPv6 aliases and exploring improvements to alias detection. We first analyze the state-of-the-art public IPv6 alias dataset, evaluating the accuracy and consistency of the alias resolutions. We uncover substantial misclassifications, motivating our development of a distinct high-confidence dataset of IPv6 aliases that enables us to correctly identify the distribution of aliased prefix sizes, detect real-world inconsistencies, and characterize the effects of different alias detection parameters. In addition, we show how small differences in the alias detection methods significantly impact address discovery (i.e., target generation algorithms). Our findings lay the foundation for how alias detection can be performed more effectively and accurately in the future.