I spent some hours last days browsing through Edward Tufte's nice book Visual Explanations. Although sometimes it gets on graphical issues way more complex than I normally need, it is a great material both on learning how to present results and on using data to support analytical thinking. So that I try to keep some of the lessons I just learned, I decided to keep a few notes here:

- On presenting data visually, ensure there is a scale and a reference for the reader

- It is often useful to look at data on scales one order of magnitude larger and smaller than the actual quantities

- Place data in an appropriate context for assessing cause and effect. This includes reasoning about reasonable explanatory variables and expected effects.

- Make quantitative comparisons. "The deep question of statistical analysis is compared to what?"

- Consider alternative explanations and contrary cases.

- Assess possible errors in the number reported in the graphics.

- In particular, aggregations on time and space, although sometimes necessary, can mask or distort data.

- Make all visual distinctions as subtle as possible, but still clear and effective. Think of elements in your displays as obeying degrees of contrast: if all (bg, axis, data, ...) have the same contrast, they'll all get the same attention. In particular, applying this to background elements clarifies data.

- Keep criticizing and learning from visual displays you find useful or not.