In the data visualization community, there are those who believe that there are universal rules such as “Never use pie charts”, or, “Always include zero in a chart’s scale”, and then there are those who believe that there are no universal rules that apply in all situations, only general principles that must be adapted to the specific situation at hand based on judgment and experience. I propose a third possibility, which is that many common dataviz design decisions can be codified as formal rules that apply in all situations, it’s just that those rules tend to be too complex to be expressed as single sentences. They can, however, be expressed as relatively simple decision trees that can reliably guide practitioners of any experience level to the best design choice.

Chart creators often extend the quantitative scale in their charts to include zero when it’s not necessary, or don’t extend it to zero when it is necessary. Making the wrong design choice can make charts hard to read, distort readers’ perception of the data, or hide key insights. Expert opinions differ on when it is and isn’t necessary to extend a chart’s scale to zero, but most of the opinions that I’ve seen are, I think, too simplistic. Determining when zero is or isn’t necessary is surprisingly complex and multi-factorial (hence this 4,600-word article), but it can be captured as a decision tree of rules that chart designers of any experience level can follow to guide them to the right choice in any situation. Feel like coming down the rabbit hole with me to find out what are all of the factors that affect this decision?