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Dirty data and deceptive design practices can undermine, invert, orinvalidate the purported messages of charts and graphs. These failures canarise silently: a conclusion derived from a particular visualization may lookplausible unless the analyst looks closer and discovers an issue with thebacking data, visual specification, or their own assumptions. We term suchsilent but significant failures "visualization mirages". We describe aconceptual model of mirages and show how they can be generated at every stageof the visual analytics process. We adapt a methodology from software testing,"metamorphic testing", as a way of automatically surfacing potential mirages atthe visual encoding stage of analysis through modifications to the underlyingdata and chart specification. We show that metamorphic testing can reliablyidentify mirages across a variety of chart types with relatively little priorknowledge of the data or the domain.

Surfacing Visualization Mirages