Correlation and Causation

Results of an experiment may suggest that the independent and dependent variables are related.

The relationship between variables, the correlation, can be positive, both variables increase or decrease together. Or negative, one increases and the other decreases.

Additionally, there may be no relationship between the variables. To determine if an apparent correlation reflects a direct cause-and-effect association, a causal relationship, additional control experiments must be performed.

For example, consider an ecosystem where geckos, parasitic ticks, and crows coexist. Crows prey on geckos, and ticks feed on animal blood.

A researcher examines five different gecko populations and finds that the number of geckos without tails decreases as the number of parasitic ticks increases, showing a negative correlation.

Based on this negative correlation alone, the researcher cannot tell whether the parasite directly causes tail loss.

However, if the researcher had counted the number of crows at each location, he may have found a positive correlation between the number of crows and the number of tailless geckos.

And after examining the crows' stomach contents, he would have also found the missing gecko tails.

Together, these observations suggest that crows cause tail loss in geckos.

But a correlation between two variables does not necessarily establish causation. A third variable may affect both variables, creating a correlation between them.