When discussing correlations and regressions it is important not to use the term

Question

When discussing correlations and regressions it is important not to use the terms "cause and effect" when describing these relationships. Why?

Why do we need to discuss the "relationships" or "correlations" between variables but not "cause and effect"?

What does it take to be able to test cause and effect? Can we ever do this in business? What do you think?

At this junction of the course I like to ask three questions about the course:

1. What is not working well in this course that would you like us to STOP?

2. What would work well that we are not doing and that we should START?

3. What is working well that would you like us to CONTINUE?

This is one of the tougher courses for several of you - so, your feedback on how we are doing is important.

Be specific. MUST BE 350 WORDS OR MORE! One Reference

Explanation / Answer

Correlation denotes the interdependency among the variables for correlating two phenomenon, it is essential that the two phenomenon should have cause-effect relationship & if such relationship does not exist then the two phenomenon cannot be correlated.

If two variables vary in such a way that movement in one are accompanied by movement in other, these variables are called cause and effect relationship. Causation always implies correlation but correlation does not necessarily imply causation.

In statistics, many statistical tests calculate correlations between variables and when two variables are found to be correlated, it is tempting to assume that this shows that one variable causes the other. That "correlation proves causation," is considered a questionable cause logical fallacy when two events occurring together are taken to have established a cause-and-effect relationship.

For example, in a widely studied case, numerous epidemiological studies showed that women taking combined hormone replacement therapy (HRT) also had a lower-than-average incidence of coronary heart disease (CHD), leading doctors to propose that HRT was protective against CHD. But randomized controlled trials showed that HRT caused a small but statistically significant increase in risk of CHD. Re-analysis of the data from the epidemiological studies showed that women undertaking HRT were more likely to be from higher socio-economic groups (ABC1), with better-than-average diet and exercise regimens. The use of HRT and decreased incidence of coronary heart disease were coincident effects of a common cause (i.e. the benefits associated with a higher socioeconomic status), rather than a direct cause and effect, as had been supposed.

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