Graphics Group @ ISU

Statistical inference provides the protocols for conducting rigorous science, but data plots provide the opportunity to discover the unexpected. These disparate endeavors are bridged by visual inference, where a lineup protocol can be employed for statistical testing. Human observers are needed to assess the lineups, typically using a crowd-sourcing service. This paper describes a new approach for computing statistical significance associated with the results from applying a lineup protocol. It utilizes a Dirichlet distribution to accommodate different levels of visual interest in individual null panels. The suggested procedures facilitate statistical inference for a broader range of data problems.