Our job as statisticians focuses primarily on the analysis and communication of uncertainty in its many aspects. For this episode, I think of uncertainty as the variability in the population we are studying, the uncertainty around estimates, or the model uncertainty, which we might capture via looking into various models.
Interestingly, the visualization of uncertainty – one of the primary forms we use to communicate it – is a rather unknown field. In this episode, I’m very excited to speak with Paolo Eusebi, who has done research in this field to answer the following questions:
- What can we learn from examples outside of medical research? How is it explained to the general public?
- What’s the current status in medical research?
- How can we become better in communication of uncertainty?
- What are further excellent resources regarding the display uncertainty?
Find the slides from Paolo here:
View also the webinar about visualization here (only for PSI members):
https://www.psiweb.org/vod/item/psi-webinar-using-visualisation-to-help-make-decisions
Resources:
- Cairo, Alberto. The truthful art: Data, charts, and maps for communication. New Riders, 2016.
- The MU Collective directed by Jessica Hullman and Matt Kay
- Wilke, Claus O. Fundamentals of data visualization. O’Reilly Media, 2019.
- PSI Visualisation Special Interest Group (VIS SIG)
- Van der Bles et al. “Communicating uncertainty about facts, numbers and science” Royal Society open science 6.5 (2019): 181870.
- Bonneau et al. “Overview and state-of-the-art of uncertainty visualization.” Scientific Visualization. Springer, London, 2014. 3-27.
- Spaghetti Plots and Hurricanes’ Paths
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Paolo Eusebi
Contract Statistician
Statistician with broad experience in all aspects of biostatistics, epidemiology and health services evaluation.
Interested in consulting offers.
Specialties: Data management, data analysis, research projects.
Knowledge of main statistical software packages such as SAS, STATA and R.
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This group was set up to help each other to become more effective statisticians. We’ll run challenges in this group, e.g. around writing abstracts for conferences or other projects. I’ll also post into this group further content.
I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.
I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.
When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.
When my mother is sick, I want her to understand the evidence and being able to understand it.
When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.
I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.
Let’s work together to achieve this.
