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Designing suitable tasks for visualization evaluation remains challenging.Traditional evaluation techniques commonly rely on 'low-level' or 'open-ended'tasks to assess the efficacy of a proposed visualization, however, nontrivialtrade-offs exist between the two. Low-level tasks allow for robust quantitativeevaluations, but are not indicative of the complex usage of a visualization.Open-ended tasks, while excellent for insight-based evaluations, are typicallyunstructured and require time-consuming interviews. Bridging this gap, wepropose inferential tasks: a complementary task category based on inferentiallearning in psychology. Inferential tasks produce quantitative evaluation datain which users are prompted to form and validate their own findings with avisualization. We demonstrate the use of inferential tasks through a validationexperiment on two well-known visualization tools.

Inferential Tasks as an Evaluation Technique for Visualization