Introduction: Summary of article
The article correlation versus causation is meant to give a different approach towards the two relationships. The article implies that correlation does not mean causation is a scientific phrase. It actually tries to put emphasis that the state of correlation between two variables barely means that one results into the other. The article continues to state that statistical methods can be used to calculate correlation between variables. However, it is also made clear that it is possible to calculate the possibility of a true causal relationship. The examples that are given involve the Granger causality test and the convergent cross mapping.
There is a however a contradicting axiom that tries to stipulate that correlation indicates proof to causation. This assumption is however questioned under the basis of logical fallacy. That is, they are further evaluated whether they result to cause and consequence affiliation (Edwards 2000). This assumption is also termed in Italian meaning, with this, therefore because of this or false cause. There are varied examples of situations that bring about the cause effect practices. They are logically illustrated with reference to life. That is, in different fields of life, there are differently itemized examples that can easily elaborate the difference or similarities between correlation and causation.
For example there is a diligently studied example to make easy the understanding of the above concepts. Numerous epidemiological studies indicated that women were into the use of hormone replacement therapy. These women had a less than average situation of coronary malady. This lead to the doctors to make uninformed decisions that it was possible that the Hormone replacement therapy resulted to protection against coronary heart ailments. It may appear absurd to state that control trials indicated that the hormone replacement therapy also resulted to minute but statistically recognizable increment in the risk of coronary Heart Disease. It was later discovered that the women going through hormone replacement therapy are likely members of the social economic regimen. That means that they possess a better diet and keep-fit schedule. The use of Hormone replacement therapy acquires a coincidence of the effects of a uniform cause. This illustrates that the benefits accrued to a higher economic status. This therefore ignores the principal of cause and effect.
Brief definition of correlation and causation
Correlation
Identification of any reason behind a certain idea or even argument is defective does not necessarily mean the conclusion does not rhyme. Here comes the idea of correlation and causation. Correlation is basically a simplified test of relationship between two distinct variables (Bordens 2011). However, it is not necessarily a cause effect relationship. That is, correlations can either be on the positive or negative. In details, to have a negative correlation indicates that as the occurrence of one item increases, the other one decline. On the other side, the positive correlation indicates that the occurrence of one event means an increase in occurrence of another thing.
Causality
On the other side, causality means a statement that indicates that if the value of one item changes then the other one change consequently. This creates a necessary but not sufficient cause versus effect relationship. It is very difficult to provide proof for causality. This is because one needs to prove that there is not only relationship between two things (Bordens 2011). One needs to provide the cause-effect relationship. For example, the statement that accidents cause death is a causality statement. This is because it states one thing causes the other.
Correlation within the article
It is clear that correlation differ spectacularly from causality. While causality tends to give the cause of a certain event, correlation tries to show that there is some change towards negative or positive side of the effects (Bordens 2011). For example, in the above example in the article, there is the use of correlation. Originally, the doctors make a conclusion based on cause-effect situation. This is when the make a decision that the Hormonal replacement therapy results causes protection against coronary heart diseases. The causality is vividly evident in the context. However, the statement of conclusion faces some objections since after a detailed research is made.
Ideally, the next conclusion is supposedly a correlation kind of conclusion. This means that after identifying that the hormonal replacement therapy does not fully result into preventive measure of the coronary heart diseases. It explains that statistics have it that only those who were economically able were diagnosed as not affected by coronary heart disease. This means that the hormonal replacement only improved the immunity against heart disease hence leading to a correlation statement.
Alternative research design and its concern
There occurs difficulty in identifying the validity correlation in conclusion making. Published reports make one wonder whether there is a causal relationship or even correlation ship. This is because the kind of report made is either made by someone who was not serious. In that case, there exists a possibility of the data being biased. Data being biased could be as a result of the relationship being discussed appearing so obvious that there is no need to carry out any research. To support this conclusion is the result that we receive from able and educated doctors. They make conclusions from mare observations (Bordens 2011). They are not quite sure whether the conclusions are as stated cause effect. However, this conclusion is the most probable that someone who will not give deeper attention into any observation will come into terms with.
The conclusion made in the example article mean that there is need for a well designed research. That is, there is need for better information which is not coincidental but has some facts behind it. Generally, it is easy to find good examples of correlation. This is the situation where it would not make sense giving chance for a causal relationship (Bordens 2011). That is, there are some straight forward situations that do not require detailed information to make an informed decision. These kinds of situations do not even call for any further research design in order to eliminate the doubt of having causal or even correlation. This means a coincidental correlation.
Ideally, there are some situations that are not coincidental. A test and a well developed research design may be used to identify whether the conclusions made were correlation by analysis or by coincidence. Qualitative method of regression where there is the use of statistics to make informed decision. Regression analysis implies ample use of statistics to make estimations among differentiated variables (Edwards 2000). It deals with two different variables. These are the dependent and the independent variables. That is, the dependent variable is the effect and the independent variable is the cause. In this case, detailed statistical estimation could be of help since it will enable the decision maker to have a basis of conclusion. This is practically different from the comfort of making assumptions and taking issues to be obvious.
With reference to the article on the hormone replacement therapy having a relationship with protecting individuals from coronary heart disease, there is need to analysis to prove their relationship. Statistical studies that are then analyzed by the use of regression method are made a necessity and sufficient requirement. Use of statistics to identify the people that have been tested to suffer from the heart disease is the first measure (Edwards 2000). After that, it is important that the people who have been taking hormone replacement therapy also need to be statistically analyzed. It is after this that a better analysis can be made. That is, it will be easy to identify the number of people who have taken the therapy and do not suffer from the coronary disease. By so doing statistical data will enable one to make a comparative decision. This enables one to to make the best relationship between the parties involved.
Despite the fact that statistics are one of the most effective means of creating conclusions, it is also disadvantageous to some extent. The use of sophisticated methods of statistical technical techniques may lead towards unreliable results (Bordens 2011). There is need for ample attention towards the meaning of data and the variables being used to express results. In that case, the use of statistics is necessary but still does not reach the sufficiency level.
Conclusively, there lies a difference between the two categories of relationships. The key difference as laid out in the paper within the descriptions and the articles lies within proof. That is, correlation does not prove causality. To a large extent, there is a vice-versa possibility of relationship. One could be the cause of the other. That is in the case of a correlation. It is important to note that there is need for some study to be made to ensure that there is effective analysis leading to making the best relationship between variables.
References
Bordens, K. S., & Abbott, B. B. (2011). Research design and methods: A process approach. New York: McGraw-Hill.
Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods. doi:10.1037//1082-989X.5.2.155
