When you find that there is a relationship between two variables then you have found a relationship and nothing more than that!

Finding a positive relationship between the number of doctors per 100 000 population and the number of cell phones per 100 000 population does not mean that more doctors will cause there to be more phones. It is more likely that some other economic factor is the cause of both more doctors and more phones. Do not speculate. Simply state that while you have found a relationship between the two variables there is no suggestion that you have any evidence about why there is such a relationship. A scatterplot, even one showing a very strong relationship, does not establish a cause between the two variables. The explanation maybe that both the variables are related to a third variable not being measured – a “lurking” or “confounding” variable.

Note: These variables are positively correlated!

• Number of fire trucks vs amount of fire damage

• Teacher’s salaries vs price of alcohol

• Number of policemen vs number of crimes

There would be a strong positive relationship (correlation) between the damage that a fire does and the number of firemen involved in fighting the blaze. Do we conclude that the firemen cause the damage? Of course, the most likely explanation of this correlation is that the size of the fire (an external variable that hasn’t been included in the study) caused the damage as well as number of firemen needed.

Cause cannot be established by plotting a scattergraph.

It is easy to draw scatterplots that are sheer nonsense. For example, the Number of McDonald’s sold vs Number of Medals won in the Sydney Olympics for countries that took part. Although the points in this scatter plot may or may not show an association, it is ridiculous to conclude that the Number of McDonald’s is related in any way to the Number of Medals won in Sydney. This is an example of a spurious correlation. The two variables may be related because they both depend on a third factor, in this case, population.