Past insights and future innovations in statistical communication design: Towards cognitive friendly statistics
Statistics Netherlands (CBS) is responsible for compiling and publishing reliable and high quality statistics of the Dutch society. Effectively communication of official statistics is important: it indicates the value for society. However, assessing quality aspects with respect to the design of visualizations and correct understanding of the message of statistical communication, have not received much attention yet. In this paper we aim to explore further directions of multi-disciplinary research to evaluate the quality of statistical information communication as a first step towards more cognitive friendly statistics. The central research question is how can statistical information be effectively visualized and communicated so that it is understandable and usable by the intended audience? As first explorations, several user evaluation studies are described were different types of visualizations, graphs, infographics, texts and pictures have been evaluated with various user groups. These findings illustrate relevant elements that indicate the perception, understanding and design quality of statistical information and communication products. Reflections on a holistic, interdisciplinary approach that unified guidelines for designing different types of statistical communication products with evidence based research, contributes to the development of quality framework for producing cognitive friendly statistics, i.e., an effective strategy of statistical information and communication.
Authors
- Vivian Meertens
- Martijn Tennekes (ORCID: https://orcid.org/0000-0002-6506-9522)
- Marco Puts (ORCID: https://orcid.org/0000-0003-2497-1760)
- Reinoud D. Stoel
- Ger Snijkers
- Nynke Krol
- Nicole Bongers
- Edwin de Jonge
Institutions
- Centraal Bureau voor de Statistiek (NL)
- Netherlands School of Public Administration (NL)
- Centraal Bureau voor de Statistiek (NL)
Publication Details
- Journal
- Statistical Journal of the IAOS
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1177/18747655251414390
- Primary Topic
- Data Visualization and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00