Nice examples, but be careful. Don’t kill the relationship between correlation and casue-efect too fast!. When there is a strong correlation between x and y, it is likely that there is a cause-effect chain involved. What you don’t know is which is the cause and which the effect. Usually, at least one of the variables (x or y)is an effect, but you don’t know which one. If x is an effect, it is possible that y is the cause or another effect from a common cause. So you may not have the cause among your variables x and y! Examples: The cause of increase in “icecream sales” is “higher temp”. The cause of increase in “shark attacks” is “more swimmers”, due to “high temps”. So the “summer” is a common cause for both effects. In the examples of the storks and the population, of course that the increase in “observations” does not increase the “population”, but the increase in “population” (cause) does increase the “observations” (effect). A strong correlation is a first step towards a cause-effect analisys. But you must be open minded and must not begin that analysis with assumptions. The only information you have is that both variables are correlated and then they have a common cause. You know that at least one of the variables is an effect. One of the variables might be that common cause itself. But the correlation chart can’t tell you which one is an effect and which one (if any) is a cause. The cause-effect analysis is needed for that.
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