Most empirical papers in psychology involve statistical analyses performed on a new or existing dataset. Sometimes the robustness of a finding is demonstrated via data-analytical triangulation (e.g., obtaining comparable outcomes across different operationalizations of the dependent variable), but systematically considering the plethora of alternative analysis pathways is rather uncommon. However, researchers increasingly recognize the importance of establishing the robustness of a finding. The latter can be accomplished through a so-called multiverse analysis, which involves methodically examining the arbitrary choices pertaining to data processing and/or model building. In the present paper, we describe how the multiverse approach can be implemented in student research projects within psychology programs, drawing on our personal experience as instructors. Embedding a multiverse project in students’ curricula addresses an important scientific need, as studies examining the robustness or fragility of phenomena are largely lacking in psychology. Additionally, it offers students an ideal opportunity to put various statistical methods into practice, thereby also raising awareness about the abundance and consequences of arbitrary decisions in data-analytic processing. An attractive practical feature is that one can reuse existing datasets, which proves especially useful when resources are limited, or when circumstances such as the COVID-19 lockdown measures restrict data collection possibilities.