storytelling with data: #43 misleading graphs

What this episode covers

A good data visualization can reveal and communicate insights. But bad graphs abound. In this episode, Cole chats with Ben Jones about misleading graphs. They discuss a number of common issues, including missorted time, miscalculated area, going against expected norms, spurious correlations, and more. Tune in for tips on how to read graphs and spot issues and avoid being duped, as well as strategies to consider when graphing data so the visualizations you create don’t inadvertently misinform. LINKS: Ben Jones | @dataremixed | Data Literacy Books by Ben mentioned: Learning to See Data, Avoiding Data Pitfalls Ben’s repository of graphical gaffes: What NOT to Do Book: Mind in Motion (Barbara Tversky) Book: Graphic Methods for Presenting Facts (Williard C. Brinton) Article: Linear vs. Quadratic Change (Robert Kosara) Article: Why not to use two axes, and what to do instead (Lisa Charlotte Rost) Article: The public do not understand logarithmic graphs used to portray COVID-19 Site: ourworldindata.org Site: spurious correlations Site/books: Calling Bullshit (Carl Bergstrom & Jevin West) Tweet/graph: Who emits the most? (Greta Thunberg) Resource: Ben’s “16 chart reading tips” checklist SWD workshops: see upcoming dates & register (use discount code PODCAST10 at checkout)

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This episode is 54 minutes long.

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This episode was published on May 6, 2021.

What is this episode about?

A good data visualization can reveal and communicate insights. But bad graphs abound. In this episode, Cole chats with Ben Jones about misleading graphs. They discuss a number of common issues, including missorted time, miscalculated area, going...

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