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| − | ====Correlation <math>\neq</math> Causation====
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| − | Even if you find the strongest of correlations, you should never interpret it as more than just that... a correlation.
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| − | <blockquote>
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| − | 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.
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| − | 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
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| − | Given any two correlated events A and B, the following relationships are possible:
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| − | * A causes B
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| − | * B causes A
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| − | * A and B are both the product of a common underlying cause, but do not cause each other
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| − | * Any relationship between A and B is simply the result of coincidence.
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| − | </blockquote>
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| − | Although a correlation between two variables could possibly indicate the presence of
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| − | * a causal relationship between the variables in either direction(x causes y, y causes x); or
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| − | * the influence of one or more confounding variables, another variable that has an influence on both variables
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| − | 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...
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