Do you know why is it so important to create a simple yet informative data visualization?
How your design choices impact the visuals?
How small details – like text alignment – influence the overall visualization?
How gestalt principles guide you?
In episode 3 of this mini-series, I discuss the impact of simplified data visualization and how to achieve it.
Specifically, I address these factors that affect your visualization:
- Clutter
- Mis-alignment
- Too many elements like
- Colour
- Fonts
- Annotations
- Header, subheader, sub-subheader
- “Unnecessary” annotations
- File names
- Source data
- Program name
- Validation status
- Understand gestalt principles
- Proximity
- Similarity
- Enclosure
- Continuity
- Connection
- Closure
Learn more on how to create simple but effective data visualization by listening to this podcast and share this with others who might learn from it!
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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.