A negative correlation is a relationship between two variables such that as the value of one variable increases, the other decreases.
Correlation is expressed on a range from +1 to -1, known as the correlation coefficent. Values below zero express negative correlation. A perfect negative correlation has a coefficient of -1, indicating that an increase in one variable reliably predicts a decrease in the other one. A perfect positive correlation, which has a coefficient of +1, indicates that an increase or decrease in one variable always predicts the same directional change for the second variable. Lower degrees of correlation are expressed by non-zero coefficents between +1 and -1. Zero indicates a lack of correlation: There is no tendency for the variables to fluctuate in tandem either positively or negatively.
Examples of negatively correlated variables include:
- Yellow cars and accident rates.
- Commodity supply and demand.
- Pages printed and printer ink supply.
- Education and religiosity.
- Conservativism and cognitive ability.
There’s a common tendency to think that correlation between variables means that one causes or influences the change in the other one. However, correlation does not imply causation. There may be an unknown factor that influences both variables similarly.