Introduction
In various scientific inquiries, our aim is always to establish the causes of something. For example, we might want to establish the true causes of autism or causes of cancer or how the drug enables a patient to feel better and less pain. Also, we can be interested in knowing which cultural practices result in environmental problems in many other aspects. In such instances, we are testing the causal models. On the other hand, scientific studies also aim to establish the correlations between variables. Correlations aim at finding out the associations of variables. Variables are aspects that researchers measure which may differ from one observation to another, for example, behavior, weight, height, lifespan, income, grade-point average and fat intake. With the variables, we can assign a value to represent the variables.
Causation and correlation
We frequently experience instances of misuse between causation and correlation. For example in a British newspaper, it provides reports from a group of teenagers who were to give information about their behaviors. The survey aimed to establish if the parents of these teenagers were smokers. As a fact, the newspaper reported that children were exhibiting delinquent behaviors had parents who smoke. The results of this study seem to indicate correlations between two variables. However the headline of this printed newspaper is entitled, “”Parental smoking causes children to misbehave” Criticalthinking.org.uk, 2006). The professor in charge of the investigation mentions that cigarettes pack should have a warning b about the prominent health warnings and various social issues. However, this is a problematic assumption. First, the correlations there might be a reverse in that delinquent children make their parents have stress making them smoke.
Another example of a correlation that is assumed to be a cause is that children with bigger feet can better spell than those with smaller feet. The odd results of this study’s explanation are that those children with bigger feet are older and not quite related to being better in spelling. As children grow they develop big feet (Paulo, 2010).
Another example is that the countries in the south have high rates of divorces and lower rates of death. There is also the example that nations adding fluoride to their water experience a high level of cancer unlike those which do not. Though there are studies that have come to such findings, these responses would make more sense if the researchers would acknowledge the differences between causation and correlations (Murch, et al., 2004).
The 1998 study kindles the opposition to vaccine firestorm that provides suggestions that autism is as a result of vaccines have no support from the General Medical Council. The participants were biased, and it made the researcher commit various ethical breaches in his work. Andrew Wakefield‘s research paper no longer exists. The Researcher analyzed the health of 12 children showing signs of colon inflammation and autism shortly after receiving the MMR vaccinations (Wakefield, et al., 1998). T
hough this study raises alarms about autism and vaccine, a closer examination of this paper indicate a range of warning signs.. The researchers in their conclusion state flatly that, we did not find a close association between mumps, measles, and the syndrome and rubella vaccine. The researcher mentions that they have managed to identify that chronic enterocolitis among children could relate to neuropsychiatric dysfunction. Additionally, they state that the onset of symptoms was after, rubella, mumps and measles immunization. And that further examination should be done in studying the symptoms and its possible relation to the vaccine). This is a tame language and only studying 12 participants makes the study to lack credibility and validity (Stehr-Green, Tull, & Simpson, 2003). Up to date, no study has established the connection between autism and MMR shots. The evidence of a-pick-and-choose recruitment method is bias leading to a discredit of its conclusion (DeStefano, 2007).
Correlation provides a statistical measure that describes the direction and size of the relationship between one or two variables. However, the correlation between variables does not automatically mean a change in one of the variables as being the reason for the change in other variables values. Causation, however, means that one event leads to the occurrence of another event which means that there is a causal relationship in two variables (The Lancet, 2010). For example, smoking has a correlation to alcoholism, but it is not the main cause of alcoholism. Rather smoking causes a higher risk of lung cancer development.
Therefore incorrectly linking causation and correlation principles will lead to posthoc reasoning in which the incorrect assumptions generations are the incorrect links of two effects. The principles of causation and correlations are crucial to all researchers and scientists. It is also important for other not -scientists who study marketing, media and politics. Knowing these principles helps to promote honest evaluation and greater understanding to individuals at all ages (Science Daily, 2013).