People often fail to notice a difference between correlation and causation. The two methods are important in analyzing statistical data, allowing one to make connections with actual-world occurrences. Frances and Mills (2020) define correlation as a numerical measure that explains the size and direction of a relationship between two or more variables. On the other hand, the two state that causation shows a link between events as it describes that one event, referred to as the effect, is the result of another occurrence, referred to as the cause. This paper looks to discuss the relationship between correlation and causation and how people can use the two in decision and policymaking. There is a key variation between correlation and causation. This difference is that it is easy to find evidence of a correlation between two variables, although it is challenging to find evidence that one event results in another. According to Mills and Frances(2020), correlations can occur due to a third party that affects both variables. An example of correlation not equaling causation is when ice cream sales increase while swimming pool deaths also increase. Therefore, consuming ice cream leads to more swimming pool deaths. In this situation, an increase in ice cream sales relates to a rise in people drowning in swimming pools. Despite the rise in the first case, it does not cause the second incident to occur. Correlation can be mathematical calculated as it usually involves using statistical measures. The symbol ‘r’ is used in a correlation coefficient when calculating correlation. ‘r’ represents a single value used to describe the degree of relationship between two variables. The coefficient ‘r’ value ranges from -1 to +1. One can use the values they obtain from a correlation equation to get a relationship’s strength and direction. A researcher can use three instances to interpret the values produced from a correlation equation. The first instance is when the value of ‘r’ is less than 0. This means a negative relationship exists between the variables. Also, when r is at zero in this occurrence, it means that the two variables move in the opposite direction. An example is when one variable increases, the other depreciates. Another instance is when r is equal to 0. In such an occurrence, one establishes that there are no relationships between the two variables and that when one variable shits in one direction, the other remains constant. The last instance is when the value of r is above 0. This indicates a positive relationship between the two variables. Additionally, in this incident, both variables’ directions are the same, where when one variable increases, the other also increases. A person can test the causality of two variables through controlled experiments. For example, when testing causation, the sample is usually divided into two, with both samples being comparable in almost the same ways. After splitting the samples, one subjects the two samples to different occurrences, and the results of the two are collected and assessed (Lim et al., 2020). An example of testing for causality of two variables can be in medical research. In such an experiment, a group of participants is split into two, where one group receives an entirely new medicine while another gets an improved drug. After the experiment, an analyst collects the data and tries to establish causation between the variables. If the two experiments produce varying outcomes, then the different experiences may have resulted in different outcomes. Statistical significance determines whether the relationship between two variables is caused by other factors other than chance alone. The statistical measure provides proof relating to a null hypothesis’s plausibility, which usually defines that an event occurs due to chance alone (Carterette, 2017) . A data set can be deemed as statistically significant when its p-value is adequately low. Usually, p-values are regarded as sufficiently low when they are less or equal to 5%. Statistical significance relates to correlation as it determines the significance of a correlation coefficient. One can find the significance of a correlation equation by calculating whether the value of r fails to lie between the positive and negative critical values. Correlation, causation, and statistical significance have a variety of applications in decision and policymaking. Correlation and causation are essential in decision-making because they can better predict future outcomes. Predicting the future allows decision-makers to use correlation and causation to determine which variables have a relationship and if they can affect each other. On the other hand, policy and decision-makers use statistical significance to assess the success rates of products. An example of how decision-makers can apply correlation, causation, and statistical significance is the release of new medications. A report by the American Diabetes Association shows that Novo Nordisk, a pharmaceutical company majoring in the management of diabetes, used the three measures before releasing its new insulin medication (Bode et al., 2019). The pharmaceutical giant revealed that after 26 weeks of randomized testing of new insulin medications with diabetic patients, most of the medicines had causation on type 1 diabetes as the drugs reduced the infection’s effects. Although calculating the statistical significance of these new insulin medications was necessary as the company can only release one of these insulins to the general public.
References
Bode, B. W., Iotova, V., Kovarenko, M., Laffel, L. M., Rao, P. V., Deenadayalan, S., . . . Danne, T. (2019). “Efficacy and safety of fast-acting insulin aspart compared with insulin aspart, both in combination with insulin degludec, in children and adolescents with type 1 diabetes: the onset 7 trial.”. Diabetes Care , 42(7), pp 1255-1262.
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Carterette, B. (2017). Statistical significance testing in information retrieval: Theory and practice. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp1387-1389.
Lim, W. W. (2020). Distinguishing causation from correlation in the use of correlates of protection to evaluate and develop influenza vaccines. American journal of epidemiology , 189(3), pp 185-192.
Mills, T., & Mills, F. (2020). correlation, causation, and David Hume. Australian Mathematics Education Journal , pp 44-48.
