In the essay “Misleading Axes on Graphs” by Carl Bergstrom and Jevin West, they describe the various ways people will misuse the axes of graphs when presenting data that in some cases, benefits the agenda of the visualization’s creator. For instance, they cite the now infamous line graph created by the National Review that purposely obscures information they found inconvenient to their priorities regarding climate change. In order to successfully convince people to see what you want them to see in the data available, there are methods that can be employed to discreetly hide the statistics you want by making adjustments to the range and scale for the axes of the graph.
The other examples in this post seem nonsensical, so for those of us with a conscious there are some tried and true methods that are suggested to avoid the trappings of creating misleading graphics.
- “Bar chart axes should include zero”: Graphs that measure dependent variables should always begin their count at zero to avoid overselling your productivity. Bar charts are designed to show the value totals in each category. The whole number.
2. “Line graph axes need not include zero” (unless the data requires it): Line graphs are designed to emphasize the change in the dependent variables - commonly measured over a specific period of time. This means that a variable can have any starting value that is inherent to the nature of what is being measured.
3. “Multiple axes on a single graph": This is not a useful technique to convince anyone of anything - the example in the essay is illegible. But it certainly would be convenient to do this if your goal is to sew confusion.
4. “An axis should not change scales midstream”: In this example, the changes in the scales were significant and it is unclear why this would seem necessary in this data presentation.
5. “An axis should have something on it”: Seems fair.
6. “Don’t invert the axes”: There is no room for artistic liberty when it comes to creating a proper graph.