Data visualization is a powerful communication tool to support arguments with numbers in a way that is accessible and engaging. However, the influx of poorly designed and misleading deceptive data visualization can be dangerous and we have to be careful of the pitfalls.
So what makes a deceptive visualization to be deceptive? I am happy to share with you a blog about deceptive visualization I read recently when I tried to find some inspiration on my own project work.
- manipulation of axis orientation/scale
as you can see here, the right side visualization has been truncated in Y axis, which makes the audience has the wrong impression about the difference between X and Y.
2. Area as Quantity (Message Exaggeration)
Alway be careful when you encoding quantitative data with size. If you map the data (quantity ) into the wrong way, say, use radius rather than areas, the result can be exaggerated seriously.
3. Inverted Axis (Message Reversal)
The x and y-axis are put upside down. This distortion leads to reversal of the message rather than an exaggeration or understatement.
Reference: https://medium.com/@Infogram/study-asks-how-deceptive-are-deceptive-visualizations-8ff52fd81239#.bi0qi7zax