Knowing What You Dont Know: Choosing the Right Chart to Show Data Distributions to Non-Expert Users

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Title: Knowing What You Dont Know: Choosing the Right Chart to Show Data Distributions to Non-Expert Users
Authors: Zubiaga, Arkaitz
MacNamee, Brian
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Date: 1-Jul-2015
Abstract: 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, however, on the question of choosing data visualisations that lead viewers to an optimal interpretation of data, especially when audiences have differing degrees of data literacy. In this paper we describe a user study on perception from data visualisations, in which we measured the ability of participants to validate statements about the distributions of data samples visualised using different chart types. We find that histograms are the most suitable chart type for illustrating the distribution of values for a variable. We contrast our findings with previous research in the field, and posit three main issues identified from the study. Most notably, however, 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 using an understanding of the limits of viewers’ data literacy to design charts effectively, and we discuss factors that are crucial to this end.
Type of material: Conference Publication
Publisher: ACM
Keywords: Machine learningStatisticsData visualisationData literacyUser studies
Language: en
Status of Item: Peer reviewed
Conference Details: Web Science 2015 Conference, Oxford, United Kingdom, 28 June - 1 July 2015
Appears in Collections:Insight Research Collection

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