Tuesday, June 25, 2013

The Disastrous Effects of Forgetting that Correlation is not Causation

At Anovisions, we look at relationships in data all the time. One constant concern is how we describe those relationships. The fact that a variable "predicts" an "outcome" variable in a regression reflects only how we set up the regression. We could swap the predictor and the outcome variable in the equation and view the same relationship from the other end, only this time the original "outcome" variable would be the "predictor." The two terms are mathematical in nature and they do not reflect cause and effect.

Cause and effect is extremely hard to prove. All we can do is identify that relationships exist, at least initially. A long way down the road from an initial study, after scientific findings from all sorts of other experiments have validated our efforts, after scientific theory has provided a reasonable model for understanding the relationships in question, after the same findings have been repeated in a large variety of populations—only then can we say, "This causes that." But the smartest among us will continue to keep the question open, assuming nothing.

 For more information about how we can help interpret your results, visit us at our website, www.anovisions.com.