A negative correlation between two variables means that one variable increases whenever the other decreases. This relationship may or may not represent causation between the two variables, but it does describe an existing pattern. Perfect negative correlation means a direct relationship always exists with a decrease in one variable always meeting with a corresponding increase in the other. Statisticians assign a negative value to negative correlations and a positive value whenever a positive correlation exists.
When two variables are correlated, they may have a similar or identical cause. The increase of one variable, in a negative correlation, may represent the increase of a factor that is directly causing the decrease of another factor. If, for example, the indoor population numbers of mice and cats are negatively correlated, then the increase in the cat population may be directly causing the decrease in the number of mice. The correlation may be unrelated, however. The presence of more cats may not decrease the number of mice directly if another unrelated factor is decreasing the number of indoor mice, such as new mouse traps.
Correlations should be investigated to determine a cause. Business planners may look at existing relationships between variables, such as consumer spending and demand for a product, as part of market analysis. However, correlations should not be interpreted as evidence of one variable causing change in another variable. Complex business environments often present many complex causes and related data with variable correlations lacking causation. For example, an increase in consumer spending and revenue may occur at the same time as positive media coverage, but it may have a different cause, such as movement into a new emerging market.