Dos and Don’ts of Data Visualisation

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.