Multiverse analysis is a scientific method that specifies and then runs a set of plausible alternative models or statistical tests for a single hypothesis.[1] It is a method to address the issue that the "scientific process confronts researchers with a multiplicity of seemingly minor, yet nontrivial, decision points, each of which may introduce variability in research outcomes".[2] A problem also known as Researcher degrees of freedom[3] or as the garden of forking paths. It is a method arising in response to the credibility and replication crisis taking place in science, because it can diagnose the fragility or robustness of a study's findings. Multiverse analyses have been used in the fields of psychology[4] and neuroscience.[5] It is also a form of meta-analysis allowing researchers to provide evidence on how different model specifications impact results for the same hypothesis, and thus can point scientists toward where they might need better theory or causal models.