Correlation does not imply causation

Sep 2018
cleveland ohio
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.[1][2] 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. This fallacy is also known as cum hoc ergo propter hoc, Latin for "with this, therefore because of this," and "false cause." A similar fallacy, that an event that followed another was necessarily a consequence of the first event, is the post hoc ergo propter hoc (Latin for "after this, therefore because of this.") fallacy.

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.[3]

As with any logical fallacy, identifying that the reasoning behind an argument is flawed does not imply that the resulting conclusion is false. In the instance above, if the trials had found that hormone replacement therapy does in fact have a negative incidence on the likelihood of coronary heart disease the assumption of causality would have been correct, although the logic behind the assumption would still have been flawed. Indeed, a few go further, using correlation as a basis for testing a hypothesis to try to establish a true causal relationship; examples are the Granger causality test and convergent cross mapping.

Correlation does not imply causation - Wikipedia
Nov 2012
Lebanon, TN
Yes that is why you have to evaluate the Data points in Statistics

I know what prompted this was when I applied this in removing those that died in accidents that have no bearing on the healthcare system.

I used datasets that have an impact on the object that is being evaluated.

(this comes from doing statistical analysis in CDC Sentinel Laboratories for 44 years.)
Nov 2012
Lebanon, TN
And in the discussion that prompted this post only ONE of those data sets would have been a causative factor in (Death Rate as an indicator of healthcare quality) The number of people that died due to being tangled in their bed sheets. This would have been an outlier as an indicator of quality of healthcare.

You see these person died and no matter how good or how bad the healthcare system would have not effect postivily or negatively on the conclusion.

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