Correlation does not imply causation For any two correlated events, A and B, the following relationships are possible: A causes B; B causes A; A and B are consequences of a common cause, but do not cause each other; There is no connection between A and B; the correlation is coincidental. Less clear-cut correlations are also possible. For example, causality is not necessarily one-way; in a predator-prey relationship, predator numbers affect prey, but prey numbers, i.e. food supply, also affect predators. The cum hoc ergo propter hoc logical fallacy can be expressed as follows: A occurs in correlation with B. Therefore, A causes B. In this type of logical fallacy, one makes a premature conclusion about causality after observing only a correlation between two or more factors. Generally, if one factor (A) is observed to only be correlated with another factor (B), it is sometimes taken for granted that A is causing B, even when no evidence supports it. This is a logical fallacy because there are at least five possibilities: A may be the cause of B. B may be the cause of A. some unknown third factor C may actually be the cause of both A and B. there may be a combination of the above three relationships. For example, B may be the cause of A at the same time as A is the cause of B (contradicting that the only relationship between A and B is that A causes B). This describes a self-reinforcing system. the relationship is a coincidence or so complex or indirect that it is more effectively called a coincidence (i.e. two events occurring at the same time that have no direct relationship to each other besides the fact that they are occurring at the same time). A larger sample size helps to reduce the chance of a coincidence, unless there is a systematic error in the experiment. In other words, there can be no conclusion made regarding the existence or the direction of a cause-and-effect relationship only from the fact that A and B are correlated. Determining whether there is an actual cause-and-effect relationship requires further investigation, even when the relationship between A and B is statistically significant, a large effect size is observed, or a large part of the variance is explained. Just saying....
Posted on: Wed, 09 Apr 2014 19:16:01 +0000
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