YSS Pre-conference Workshop 2026
The practice of using data, visuals, and narrative to communicate insights clearly and effectively.
Eases interpretation by giving data context and meaning
Makes key insights more memorable through narrative
Highlights what matters, helping audiences focus on the takeaway
It’s cherry picking facts.
You’re forcing the data to support a specific argument.
It suggests a causal claim when there isn’t one.
It oversimplifies complicated relationships.
How do you choose which variables to include in a model?
You’re expected to do the work and make statistically valid, ethical choices. Why would data storytelling be any different?
Data is collected by someone. Data is collected for something. Data collection is funded by someone.
Who/what is represented by that data? Who/what is not represented by that data?
Letting the data speak for itself without context is not a neutral choice.
Data is not neutral. You are not neutral. Accept and acknowledge that as part of the story.
Same story. Same data.
Different audience.
Different charts. Different words.
Who is your audience?
What do they already know?
What do you want them to know (and/or do)?
Social media, blog posts, or press release: limited statistics knowledge, needs the main message
Conference presentation: assume statistics knowledge but not in specific area, need some detail
Journal article: assume more knowledge, Need all the details
Might all be in the one publication (title/abstract/main paper)
What does everybody need to know?
What do some people need to know?
What do only expert users need to know?
First chart showing patterns across all data. Further charts showing patterns by demographic groups/regions/etc.
Use animation and interaction (carefully) to progress the story.
Don’t just tell them what they already know.