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 editorsHelske, Jouni; Helske, Satu; Cooper, Matthew; Ynnerman, Anders; Besancon, Lonni

Journal or seriesIEEE Transactions on Visualization and Computer Graphics

ISSN1077-2626

eISSN2160-9306

Publication year2021

Volume27

Issue number8

Pages range3397-3409

PublisherIEEE

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/TVCG.2021.3073466

Research data linkhttps://github.com/helske/statvis

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/75850

Publication is parallel publishedhttp://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.


Keywordsstatistical methodsBayesian analysisinferenceconfidence levelsstatistical graphicsvisualisationinterpretation (cognition)

Free keywordsstatistical inference; visualization; cliff effect; confidence intervals; hypothesis testing; Bayesian inference


Contributing organizations


Related projects


Ministry reportingYes

Reporting Year2021

JUFO rating3


Last updated on 2024-11-03 at 14:29