Dos and Don’ts of Data Visualisation
Whether on purpose or not, a statistician can mislead an audience with a chart. This article explains some chart design principles and common mistakes novice data analysts make. Think about the statistical charts you have seen on billboards, in the news, and in research studies. Using these principles as a guide, would you classify any of those charts as misleading? Be sure to take note of the suggestions for successful dashboards.
Make charts correct
Don't confuse correlation with causation
Quite often superimposing time series of two different measurements will show a strong correlation. It is an easy mistake to confuse correlation with causation. Bias can make us conclude that one thing must cause another if both change in the same way and at the same time.
For example, if you plot two different data series (A and B) on the same chart, you may notice that both follow a similar pattern over time. It is extremely hard, if not impossible, to prove that A caused B or vice versa. There are so many third unplotted factors that may influence both A and B. Only a large profound statistical-based study on all factors can give some indication of causation, if any exists.
