How to Lie with Data Visualization

The way in which data is presented to us can completely change the way we interpret it and the message it is trying to send us. Certain representations of data can make for a more clear argument as well as a more skewed argument. While the data may be correct, the way that it’s presented can trick us into either beleiving something incorrect or only focusing on a very specific aspect.

How Deceptive are Deceptive Visualizations

This reading covers many of the same ideas that the blog post did, but in much more depth, and with confirmation of the ideas from the empirical study. I found the truncated and inverted axis techniques to be the most decieving to me. They are especially decieving if one is only looking at the image for a quick period of time. For example, with a truncated axis, one is unlikely to see whether or not the y axis minimum/maximums have been enlarged, and even if one does, it still raises the question of whether or not this change was done intentionally as a means of deception, intentionally to highlight some piece of information, or just unintentionally.