Correlation refers to the statistical relationship between two variables. If two variables X and Y correlate with each other, this means that a change in variable X will cause a change in variable Y with a certain probability. Although there is a reciprocal relationship, a correlation does not mean that a change in variable X will necessarily cause a change in variable Y. A distinction is made between a positive and a negative correlation.

A distinction is made between a positive and a negative correlation. In the case of a positive correlation, the values of both variables increase together. So if the values of variable X increase, this leads to an increase in the value of variable Y. Conversely, a negative correlation means that an increase in the values of variable X causes the values of variable Y to decrease. An example of a positive correlation could be the number of hours spent preparing for an exam and the grade received. If the time spent increases, you tend to do better in the exam. In contrast, there is a negative correlation between the number of hours a person sleeps and their stress level. If a person sleeps less, their stress level increases.

In the scientific context, the term causality is often used in addition to correlation. These two concepts are often confused or equated with each other. This can lead to false conclusions. A correlation does not imply a cause-and-effect relationship (causality). Just because there is a correlation between two variables does not automatically mean that a change in one variable necessarily causes a change in the other variable. This small but subtle difference is important. It prevents incorrect conclusions being drawn from data.

The following example explains the difference between causality and correlation:
The number of storks in Switzerland decreased over decades at the same time as the fertility of the population (number of babies measured against the population size). It would be wrong to conclude from this that the decline in the human birth rate is due to the decline in the stork population. Instead, both results can be causally explained by other causes that occurred at the same time: The industrialisation-induced increase in prosperity has led to smaller family sizes. At the same time, the drainage and overbuilding of many lowland areas has led to a reduction in the habitat and food supply for storks.

For a decision-making process, it is essential to analyse the actual causal relationships.