Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.

Correlation versus Causation

If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative correlation, respectively, between the two variables. If this relationship is indirect, then it is due to a correlation. However, a direct relationship would signify causation.

For example, if a researcher wants to determine the cause of tail loss in five different gecko populations and finds a negative relationship between the number of geckos without tails and the number of parasitic ticks in crows, this result would constitute a negative correlation and indicate that the crow parasite is not directly causing tail loss in geckos.

However, if the crows had been counted near each gecko population, a positive relationship between the number of crows and tailless geckos may have been found. If, after examining the contents of the crows' stomachs, the missing gecko tails were discovered, the number of crows would have directly determined the number of tails lost by the geckos—indicating causation. The correlation could have been coincidental if the gecko tails had not been found in the crows' stomachs.

Importantly, in this example, there is a negative correlation between the number of crows and the number of parasitic crow ticks. The number of crow parasites is likely to increase with increasing numbers of crows, a positive correlation. In this scenario, the number of crow ticks would also positively correlate with the number of tailless lizards. However, unlike the relationship between populations of crows and tailless lizards, the numbers of ticks and tailless lizards are not causally related.