Don't Confuse Correlation with Causation Correlation is a measure of the degree to which two variables are associated. It is important not to confuse correlation with causation.
Spurious correlation is a frequent flaw in forecasting models.
The consequences are inaccurate forecasts and misleading strategic advice about how markets work. There are three reasons why two variables may display a significant correlation. In developing a forecasting model, it is important to avoid spurious correlations. For example, one may observe a positive correlation between ice cream sales and the rate of plant growth but there is a common cause and that is temperature. Sometimes the common cause is something like population or employment growth.