Whether it stems from participant attrition, nonresponse, unwillingness to disclose information, technical errors, or flawed collection methods, incomplete data pose significant challenges to researchers in psychology. Although a rich methodological literature exists, applied researchers often lack clear guidance for aligning missing-data methods with study design, assumptions, and analytic goals. In this article, I provide a practical, assumption-aware framework for reasoning about missing data in psychology, emphasizing how missingness operates as a selection process and how method choice depends on the underlying data-generating structure. I review commonly used approaches, including likelihood-based estimation, multiple imputation, Bayesian data augmentation, and pattern-mixture models, highlighting their assumptions, strengths, and limitations. To support implementation and pedagogy, I introduce DataPatch, an interactive tool that allows users to simulate missing-data mechanisms, apply alternative handling strategies, and examine their consequences for estimation and interpretation (davidmoreau.shinyapps.io/DataPatch/). Together, the conceptual framework and accompanying tool aim to promote more transparent, principled, and informed handling of missing data in psychological research.