ABSTRACT
Statistical correlation is a method of determining if two sets of data can be related to each other. For example, the thickness of tree rings can be correlated with the amount of rainfall. So, in wet years, tree rings appear larger than in dry years. In this particular case, there appears to be a causal relationship-more rainfall (water) results in more growth. Correlations do not have to be positive; there can be a negative correlation between two variables. For example, a commodity price may go down as the supply increases. In many such cases, the link between the variables is obvious. A correlation between an increase in fertilizer and an increase in yield is another example where this link is obvious. Oftentimes, however, correlations occur where there is no apparent causal relationship. A rather famous example is foot size in children and spelling ability. There is a strong positive correlation between these two items. The reason there is such a good correlation between the two is not because foot size increases spelling ability. The association has to do with the child’s age and level of education. This is an important point about correlation; it is not a cause and effect relationship. Correlation in this context can help identify relationships that might ultimately be a cause and effect relationship. They also can be unrelated as in the foot size and spelling ability. Therefore, interpretation of correlation results should be approached with caution, particularly if there is no obvious mechanism for the two to be linked.
