Section: New Results

Increasing the Transparency of Research Papers with Explorable Multiverse Analyses

Participants : Pierre Dragicevic [correspondant] , Yvonne Jansen [CNRS] , Abhraneel Sarma [University of Michigan] , Matthew Kay [University of Michigan] , Fanny Chevalier [University of Toronto] .

Figure 5. Examples of explorable multiverse analyses.

We presented explorable multiverse analysis reports, a new approach to statistical reporting where readers of research papers can explore alternative analysis options by interacting with the paper itself [34]. This approach draws from two recent ideas: i) multiverse analysis, a philosophy of statistical reporting where paper authors report the outcomes of many different statistical analyses in order to show how fragile or robust their findings are; and ii) explorable explanations, narratives that can be read as normal explanations but where the reader can also become active by dynamically changing some elements of the explanation. Based on five examples and a design space analysis, we showed how combining those two ideas can complement existing reporting approaches and constitute a step towards more transparent research papers. This work received a best paper award at ACM CHI.

More on the project Web page, including interactive demos: https://explorablemultiverse.github.io/.