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Knowing What You Don’t Know: Choosing the Right Chart to Show Data Distributions to Non-Expert Users


Arkaitz Zubiaga, Brian MacNamee

Publication Type: 
Refereed Conference Meeting Proceeding
An ability to understand the outputs of data analysis is a key characteristic of data literacy and the inclusion of data visualisations is ubiquitous in the output of modern data analysis. Several aspects still remain unresolved, how- ever, on the question of choosing data visualisations that lead viewers to an optimal interpretation of data, especially when audiences have di ering degrees of data literacy. In this paper we describe a user study on perception from data visualisations, in which we measured the ability of partici- pants to validate statements about the distributions of data samples visualised using di erent chart types. We nd that histograms are the most suitable chart type for illustrating the distribution of values for a variable. We contrast our ndings with previous research in the eld, and posit three main issues identi ed from the study. Most notably, how- ever, we show that viewers struggle to identify scenarios in which a chart simply does not contain enough information to validate a statement about the data that it represents. The results of our study emphasise the importance of us- ing an understanding of the limits of viewers' data literacy to design charts e ectively, and we discuss factors that are crucial to this end.
Conference Name: 
WebScience Conference
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Publication Date: 
Conference Location: 
United Kingdom (excluding Northern Ireland)
National University of Ireland, Dublin (UCD)
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