Causation is a relationship between two quantities where one quantity is affected by the other. That is, the change in one quantity directly affects the other. When there is causation between two data sets, that is said to be a causal relationship. Lastly, causation implies correlation. Consider a situation where an employee earns an hourly wage. Since the number of hours worked directly affects the worker's income, there is both a positive correlation and a causal relationship. The more hours the employee works, the more income the worker makes. However, the converse is not true. While two data sets can be correlated, they might not have a causal relation. For example, consider the number of factories and the number of teachers in a city. There could be a positive correlation between these two numbers because both tend to increase as the city's population increases.

However, it is unlikely that making more factories will cause an increase in the number of teachers. Thus, it can be said that there is no causal relationship. In this case, a third factor — population size — seems to directly affect the two data sets in question. That further provides evidence against a causal relationship between the number of factories and the number of teachers.