A1 Journal article (refereed)
Can visualization alleviate dichotomous thinking : Effects of visual representations on the cliff effect (2021)
Helske, J., Helske, S., Cooper, M., Ynnerman, A., & Besancon, L. (2021). Can visualization alleviate dichotomous thinking : Effects of visual representations on the cliff effect. IEEE Transactions on Visualization and Computer Graphics, 27(8), 3397-3409. https://doi.org/10.1109/TVCG.2021.3073466
JYU authors or editors
Publication details
All authors or editors: Helske, Jouni; Helske, Satu; Cooper, Matthew; Ynnerman, Anders; Besancon, Lonni
Journal or series: IEEE Transactions on Visualization and Computer Graphics
ISSN: 1077-2626
eISSN: 2160-9306
Publication year: 2021
Volume: 27
Issue number: 8
Pages range: 3397-3409
Publisher: IEEE
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1109/TVCG.2021.3073466
Research data link: https://github.com/helske/statvis
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/75850
Publication is parallel published: http://128.84.4.34/abs/2002.07671
Abstract
Common reporting styles for statistical results in scientific articles, such as \pvalues\ and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the p-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recommended to reduce such dichotomous interpretations but their effectiveness has also been challenged. We ran two experiments on researchers with expertise in statistical analysis to compare several alternative representations of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in researchers' subjective confidence in the results. We also asked the respondents' opinions and preferences in representation styles. Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations measured as the cliff effect: the sudden drop in confidence around p-value 0.05 compared with classic CI visualization and textual representation of the CI with p-values. All data and analyses are publicly available at https://github.com/helske/statvis.
Keywords: statistical methods; Bayesian analysis; inference; confidence levels; statistical graphics; visualisation; interpretation (cognition)
Free keywords: statistical inference; visualization; cliff effect; confidence intervals; hypothesis testing; Bayesian inference
Contributing organizations
Related projects
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- Helske, Jouni
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2021
JUFO rating: 3