issing values that are missing not at random (MNAR) can result from a variety of missingness processes. However, two fundamental subtypes of MNAR values can be obtained from the definition of the MNAR mechanism itself. The distinction between them deserves consideration because they have characteristic differences in how they distort relationships in the data. This has implications for the validity of statistical results and generalizability of methodological findings that are based on data (empirical or generated) with MNAR values. However, these MNAR subtypes have largely gone unnoticed by the literature. As few studies have considered both subtypes, their relevance to methodological and substantive research has been overlooked. This article systematically introduces the two MNAR subtypes and gives them descriptive names. A case study demonstrates they are mechanically distinct from each other and from other missing-data mechanisms. Applied examples are given to help researchers conceptually identify MNAR subtypes in real data. Methods are provided to generate missing values from both subtypes in simulation studies. Simulation studies for regression and growth curve modeling contexts show MNAR subtypes consistently differ in the severity of their impact on statistical inference. This behavior is examined in light of how relationships in the data become characteristically distorted. The contents of this article are intended to provide a foundation and tools for organized consideration of MNAR subtypes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).