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This week we’re exploring causation and correlation.
- Why is it a fallacy to confuse causation and correlation?
- Provide an example of a statement that confuses causation with correlation.
- In addition to your main response, you must also post substantive responses to at least two of your classmatesâ€™ posts in this thread.
Correlation does not necessarily implicit causation. It is a phrase in statistics and science that emphasizes that a correlation between two variables does not directly imply that one causes the other. Those two are confused frequently. The human brain likes to see things similar and likes to find patterns. Causations directly applies to cases that action A is what causes the outcome of action B. This meaning, that a variable is the direct cause of the other variable . Just like a cause and an effect. Where as correlation is just a relationship. For example, action A relates to action B, however one things does not automatically cause the other event to happen.
“The primary reason as to what causes confusion between causation and correlation is when the confounding variable is ignored. For instance, one might assume that the reduction of the bees has taken place at the same time as the reduction of the coal demand. Therefore, one might assume that using less coal has led to the poor health of beehives and the reduction of bees.
This is how a correlation is confused with causation. However, the confounding variable here is the increase in pollution. It is the increase in pollution that has caused the bee population to decrease in the last few decades. Furthermore, during the last few decades, it was coal use and mining that was one of the top causes of pollution. Therefore, the confounding variable is the air pollution which is related to both bee population and coal use.”
Causation is the action of causing something. Correlation is the process of two things showing how strongly related they are to one another. It is said that correlation doesnâ€™t prove causation. It is considered a fallacy to confuse the said two because he or she is assuming that the â€œfact that one event came after another establishes that it was caused by the otherâ€ (Moore, et. al., 2020). The fallacy that confuses causation and correlation is the fallacy of weak induction known as post hoc, ergo propter hoc which means, after this, therefore because of this. â€œ[It] assumes that the timing of two variables relative to each other, in and of itself, is sufficient to establish that one is the cause and the other is the effect however, it is incorrectâ€ (Moore, et. al., 2020). In short, it is fallacious to conclude that one occurrence causes the other because there may be another cause that explains the outcome. It may be that there is a common cause for both of the events or it may be that there is a different cause all together, or maybe it was just a coincidence.
One example that many people mightâ€™ve heard of is Autism and the MMR vaccine. Autism can first be seen or noticed between 18 months and 6 years of age. The first dose of MMR vaccine is usually given to babies around 12 to 15 months and the second dose is then given around 4-6 years. The initial signs of Autism are seen after MMR vaccine. Therefore, MMR vaccine causes Autism. This is a really high correlation that many people connect to imply the causation. In order to test this hypotheses to see of it actually works, is by doing a controlled experiment on the children where one group gets the MMR vaccine and the other group does not.
As for the children jumping when the clock strikes twelve on New Years, one way to test this is by actually doing the test. This can be done so by starting a randomized controlled experimentâ€” â€œone in which subjects are randomly assigned either to an â€œexperimental groupâ€ (E) or a â€œcontrolâ€ (C), which differ from one another in only one respect: subjects in the E group are subjected to the suspected causeâ€¦â€ (Moore, et. al., 2020). It might be a long test, in terms of years, because their height will have to be checked every so often. However, one thing that has to be considered is their DNAâ€” maybe the parents are tall? Maybe not? I would collect two random groups each with 50 children of age 6. Every year Group 1 will jump when the clock strikes midnight on New Years and Group 2 will not jump. Their height will be measured before the jump and then after they jump and every year as the jumps continue.
Moore, B. N., & Parker, R. (2020). Critical thinking. New Yo4k, NY, United States of America: McGraw-Hill Education
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