Spurious Correlations

In statistics, spurious correlations is a mathematical relationship in which two or more events or variables are not casually related to each other, but it may be wrongly inferred they are, due to either coincidence or the absence of third reason.

It’s well known that correlation doesn’t imply causation. However, when lines, bars, and points have similar trend, we start to believe that one may be the cause and one may be the result.

There are several ways would cause spurious correlations:

1. Axis scales: either x or y axis scale that measures different values can’t be paired in a single graph especially those showing similar curves.
2. Change scales: although x and y axis measure same value, the scale of either event change and the proportion and range is different. The graphs below obvious show that in different range those two events highly relate to each other. However, in same range, those two events are irrelevant with each other.
3. Ifs and thens implying cause and effect: comparing two unrelated data sets together may lead to a misunderstand of causation. We can use to different present skills to examine the causation:

If Pandora loses less money, then more music is copyrighted.

However, this graphs doesn’t show that correlation:

Reference: https://hbr.org/2015/06/beware-spurious-correlations