Abstract

Introduction
In the toolset of making statistics comprehensible for users of various literacy levels, visualisations play an ever-growing part. Static and dynamic charts, maps, infographics all support understanding – or do they necessarily do so?

Methods / Problem statement
There have been numerous researches on the perception of graphs, testing various elements such as colour or chart types, ability of users to grasp size and pattern. HCSO also conducted a research on some graphical solutions a few years ago, the results of which served as a basis of our strategy in establishing our portfolio of visualisation types. However, technology does not cease to evolve, and neither do user expectations and preferences. Another factor inspiring us to explore this field is that we still lack information on how users actually make use of such products, how they interpret them and what types they find the most appropriate. In this era of infobesity and half-truths, we deem it especially important to apply efficient and correct visualisations, support users the best we can in understanding statistics, and especially helping younger generations find the truth and applicability in statistics.

Results / Proposed solution
One important aspect of our research will be to find out more about the preferences and opinions of users on visualisation. Subjective views and perceptions of our users providing one source of information, we also intend to find out more about various types of data visualisations concerning
• their efficiency on the whole in transmitting a message
• their elements (components) supporting or hindering correct interpretation
• possibilities of misunderstanding from the users’ part.
We assume that there might be a difference not only depending on the chart types and purpose of use as well as the statistical literacy levels of users but a generation gap may also exist.
Through quantitative and then qualitative methods we intend to examine users belonging to various age-groups in order to reveal whether their age or their statistical literacy level influences the interpretation of data visualisations as well as their expectations concerning these.
Methods to examine behaviour and perception of users will be making the subjects of the ‘experiment’ look for information on visualisations, choose the visualisations that they assume correspond with the verbal description of a phenomenon or process, and discuss elements of graphs that they find easy or difficult to understand in detail.

Conclusions
This research can help us outline a more up-to-date vision on data visualisations portfolio, including a subsite targeting young people and teachers, and we also hope to contribute to the general pool of knowledge on this topic, where statisticians of other NSIs can also find justification of their methods or inspiration to improve these.