  Multiverse analysis, a paradigm for statistical analysis that considers all
combinations of reasonable analysis choices in parallel, promises to improve
transparency and reproducibility. Although recent tools help analysts specify
multiverse analyses, they remain difficult to use in practice. In this work, we
identify debugging as a key barrier due to the latency from running analyses to
detecting bugs and the scale of metadata processing needed to diagnose a bug.
To address these challenges, we prototype a command-line interface tool,
Multiverse Debugger, which helps diagnose bugs in the multiverse and propagate
fixes. In a qualitative lab study (n=13), we use Multiverse Debugger as a probe
to develop a model of debugging workflows and identify specific challenges,
including difficulty in understanding the multiverse's composition. We conclude
with design implications for future multiverse analysis authoring systems.
