While circumstance can tend to be pretty clear and straightforward, assuming that such a scenario is true may lead to wrong conclusions. For example, simply because event C is evident, does not imply that B causes A. A statistical research falling into such a trap would not be taken seriously. Often there are numerous relationships between variables that can cause a given outcome and may be that it is such factors that cause the correlation. Correlation is not causation simply means that because two events or objects correlate, it is not automatic that one is the cause of the other. An example of one instance where I have seen correlation being misinterpreted as causation , is the assumption that since many people in Australia tend to smoke during the cold season and less during the hot seasons, the scenario implies that cold weather is the cause of smoking, which is not always the case. It may be assumed that cold seasons tend to overlap with smoking among people in Australia.
Often suspicions and preoccupations about the way things take place lead many to make such a general assumption from correlation direct to causation lacking strong evidence. Although the statement has some elements of truth, it is far from the reality of the relationship between things and how they occur. The mantra, correlation does not mean cause does not imply to the fact that simply because there is some connection or interrelationship between given variables does not point to the fact that one is the resulting effect of the other. Although there may be some form of correlation or relationship, it cannot be all that obvious just by a look alone. Such a scenario often requires in-depth research, investigation, and analysis for a conclusive decision to be made. Thus, proving that cold season in Australia is a causation to smoking would require an in-depth research instead of just making a blanket conclusion without any scientific and medical proof.
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