Respond to 2 discussions that I have written. Each response should be at least 150 words in length and labeled 1 and 2. Write it like a conversation and I will provide an example of what they should look like.
DISCUSSION 1
To me, the term correlation is the relationship between two things. I think that if you split the word into two parts then you get a better grasp on what I am trying to express. Lets start with the beginning of the word (co). In this I derive the meaning of together or along with one another. I then look at the latter part of the word and drop one of the r’s off so that just (relation) is there. In this we know that it means the information or data that is shared between two things.
As stated in the textbook, a correlation is “an expression of the degree and direction of correspondence between two things” (Cohen & Swerdlik, 2018). While knowing that correlation deals with the relationship between two variables, it does not cause or effect the other. It is understandable to believe that they are many things that have a positive or negative correlation within their relationships. But while discussing causation, we know that this is simply a cause and effect factor and not dealing with the relationships between two variables but how one variable or the other may have an effect upon the other.
In Romans 8:19-21 we read, “For the creation waits with eager longing for the revealing of the sons of God. For the creation was subjected to futility, not willingly, but because of him who subjected it, in hope that the creation itself will be set free from its bondage to corruption and obtain the freedom of the glory of the children of God” (ESV). Thus affirming the topic of cause and effect and how it can be sound as though it is correlation but it is not. An instance in which we may see correlation and could easily be confused with the likes of causation is the relationship between and individuals level of schooling and their income relationship. While it gives of the appearance of a cause and effect situation at first, it does not necessarily mean that the results are going to be heavily tilted in the favor of those that are administered the test towards individuals that have attended more schooling. This would also be a great correlation because the data would be easily verifiable.
DISCUSSION 2
Correlation is something that helps in research in comparing two things. Simply stated, correlation is an expression of the degree and direction of the correspondence between two things (Cohen & Swerdlik, 2017). It helps to understand relation providing a viewpoint for some similarities as well as differences. Although correlation does not imply causation, there is an implication of the prediction (Cohen & Swerdlik, 2017). If there seems to a high relationship between the variables one can, they further have some type of acknowledge what the results will be. They have a relationship however they are not the same thing. It must be emphasized that a correlation coefficient is merely an index of the relationship between variables, not an index of the casual relationship between two variables (Cohen & Swerdlik, 2017).
Introductory statistics teaches us that correlation does not imply causation (Huang, Sui 2013). There may be some useful aspects of it, but it doesnt state directly to causation. Thus, unintentionally, equating correlation with causation is warranted in complex, networked systems where positive feedback loops are a characteristic feature and entail mutual causation (Huang, Sui 2013). Understanding these relationships can help aid in the future of studies as well as the way studies may be looked at. This can help in the research of fighting certain diseases such as cancer and help find a more optimal approach. I think that correlation and causation can be easily draw together, but it is important in understanding the essential aspects of each. Symptoms lead to diseases and that is something that needs to be paid more attention in order to treat in a more effective and appropriate way. Of course, symptoms are only correlated to, and not the cause of, the pathology (Huang, Sui 2013). The details are important in leading to the correlation of the results but knowing the difference in correlation and causation is the first step in understanding research in science or any other field for that matter.
EXAMPLE:
You did an awesome explaining correlation and causation. These are terms which are mostly misunderstood and often used interchangeably. Understanding both the statistical terms is very important not only to make conclusions but more importantly, making correct conclusion at the end. In the end of reading your post I was definitely more aware of why correlation does not necessarily always imply causation. The example you used was a great choice. In this example the two events, viewing violent television and exhibiting violent behavior, are correlated. Hence, if a child is violent, it is very likely that he or she has also watched violent television. However, the violent television itself is not necessarily what caused the violent behavior. The child could exhibit violent behavior for any number of reasons. Again nice job, and thank you for sharing.
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