2020/02/17 by Jouni Helske, Satu Helske, Matthew Cooper +2 · 1 voice · 2 citations
Computer Science · Decision Sciences · Mathematics · Psychology · #Data Visualization and Analytics #Mental Health Research Topics #Meta-analysis and systematic reviews #cs.HC #stat.OT
paper · pdf · doi:10.1109/tvcg.2021.3073466
published as IEEE Transactions on Visualization and Computer Graphics. 2021; 27(8)
arxiv published 2020/02/17 · openalex publication_date 2021/04/15 · arxiv created 2021/05/28 · arxiv updated 2021/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Common reporting styles for statistical results in scientific articles, such as p-values 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.