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Correlation Causation
Even if you find the strongest of correlations, you should never interpret it as more than just that... a correlation.
Causation indicates a relationship between two events where one event is affected by the other. In statistics, when the value of one event, or variable, increases or decreases as a result of other events, it is said there is causation.
Let's say you have a job and get paid a certain rate per hour. The more hours you work, the more income you will earn, right? This means there is a relationship between the two events and also that a change in one event (hours worked) causes a change in the other (income). This is causation in action! https://study.com/academy/lesson/causation-in-statistics-definition-examples.html
Given any two correlated events A and B, the following relationships are possible:
- A causes B
- B causes A
- A and B are both the product of a common underlying cause, but do not cause each other
- Any relationship between A and B is simply the result of coincidence.
Although a correlation between two variables could possibly indicate the presence of
- a causal relationship between the variables in either direction(x causes y, y causes x); or
- the influence of one or more confounding variables, another variable that has an influence on both variables
It can also indicate the absence of any connection. In other words, it can be entirely spurious, the product of pure chance. In the following slides, we will look at a few examples...
