Cassie Ann Short, Nate Breznau, Maria Bruntsch, Micha Burkhardt, Niko A. Busch, Elena Čėsnaitė, Maximilian Frank, Carsten Gießing, Daniel Krähmer, Daniel Kristanto, Tina B. Lonsdorf, Claudia Neuendorf, Hung Hoang Viet Nguyen, Manuel Rausch, Xenia Schmalz, Andreas Schneck, Cem Tabakci, Andrea Hildebrandt
Multiverse analysis offers a comprehensive response to a core vulnerability in empirical research: the uncertainty of scientific conclusions arising from defensible yet flexible data-processing and -analysis decisions. By systematically mapping and computing all or a sample of all plausible data-processing pipelines, multiverse analysis reports the robustness of findings across analytical flexibility and increases transparency in the research process. As its adoption grows across disciplines, so too does the need for clarity on how to design, report, and interpret multiverse results responsibly. In this article, we provide interdisciplinary guidance on key procedural considerations, including defensibility and equivalence evaluations, preregistration, and computational demands. We aim to harmonize terminology, promote best practices, and foster conceptual cohesion across fields, supported by reference to domain-specific resources when appropriate. By doing so, we contribute to the broader movement toward more robust, reproducible, and transparent science, one that not only reports results but also interrogates the analytical pipelines that produce them.