Correlation and Causation
It is essential to know the difference between correlation and causation in statistics. The need to stems from the fact that statistical data is considered significant in decision making when solving diverse solutions. Raw data does not add any value to decision-makers. The data must therefore be subjected to different analysis procedures to convert it into a form that relays meanings hence allowing people to make better decisions. There are diverse approaches to analyze data and present it in a meaningful way. Two such approaches of presenting data that had meaning are by determining correlation and causation. Many people perceive correlation and causation to be the same. That is not true. Even though the correlation is considered to be the same with causation, slight differences make the two independent of each other.(Correlation and Causation Essay-Example)
As the name suggests, correlation is a statistical method that measures the relationship between two variables. The relationship is measured based on a linear association between the involved variables. Correlation is computed to determine the strength of the relationship in the study variables. Different types of correlation emerge from computing the relationship between variables. A positive correlation means that the variables are positively related. It means that the variables move in the same direction when subjected to different conditions, for example, an increase in one variable results in the same effect for the other and vice versa. A negative correlation means that the variables are oppositely influencing each other; for example, one decreases with the increase of the other. The last type of correlation is no relationship. The measure presented by a zero-mean that there is no correlation between the study variables (Gogtay & Thatte, 2017). The correlation between different variables is given by the formula:.(Correlation and Causation Essay-Example)
Where rxy refers to the correlation coefficient that exists in the relationship between the variables y and x.
Xi represents the diverse of the x values involved
x̅ represents the mean of the x values
yi standing for the y valuables and
ȳ representing the mean y values.
Similarly, a person can avoid the hectic calculations involved in finding correlation by the use of statistical software tools such as the CORREL function in Excel. The correlation is automatically computed provided that the correct variable entries have been made.
Causation, on the other hand, refers to the statistical measurement of the relationship between variables and their effect on each other. As the term implies, the measurement looks into the cause-effect of the variables involved. The variables in causation can either appear at the same time or after another. Likewise, the occurrence of one variable must cause the others also to take place. There is no specific formula for causation. Diverse experimental studies have to be carried out to determine if the investigated variables have a causal effect on each other. The experiments involve subjecting diverse groups into different treatments to determine causative effects.
The difference between correlation and causation lies like the variable’s relationship. While correlation only focuses on establishing the existence of a relationship between two variables, causation delves further to determine the effect of the relationship on a particular or all variables. An example of correlation is seen in data showing the existence of a relationship between cases of vehicle robbery and homicide. The example proves the existence of a relationship that is neither caused by the two variables. Causation, on the other hand, is depicted by reviewing the relationship between insecurity and car theft or homicide. A rise in insecurity cases results in a similar pattern occurring in vehicle robberies or homicide. Insecurity, as a variable causes an increase in vehicle robberies and homicide.(Correlation and Causation Essay-Example)
Statistical significance refers to a test done to determine the viability of the null hypothesis. The function of statistical significance is to prove that the findings used valuable data sample. A statistically significant study proves the null hypothesis by using reliable and consistent data (Amrhein & Greenland, 2018). The significance test, therefore, proves that the findings were not just attained by lack and that repeated actions will yield similar results. A small p-value shows the findings are statistically significant, with a large one failing to prove the null hypothesis despite the consistency of the data (Ioannidis, 2018). Statistical significance is associated with correlation through the p-value. Determining the reliability of the linear model of a set of data does not depend on the relationship value, r, alone (Ganti, 2020). On the contrary, the number of the population used must be proved significant for the research. The p-value represents the population’s significance.
Knowledge of correlation and causation is broadly applied in criminal justice. Correlation is used to investigate the relationship between the socioeconomic environment and crime. Investigators may choose to focus on the various crimes that occur in a specific place and link it to the environment. An example is seen in determining the relationship between sexual assaults and burglaries in middle-income households. The findings may reveal a positive relationship, thereby leading to investigations on how the neighbourhood contributes to such acts. Causation, on the other hand, is used to determine the different factors contributing to certain crimes. Understanding the factors that lead to crime, therefore enlightens policymakers on the ideal strategies to take in ending the crime (Walters & Mandracchia, 2017). The policies involve tackling the causative factors that lead to detrimental security effects. For instance, the statistical finding may show that the use of drugs causes a rise in suicides arising from depression in the community. Policymakers may, therefore decide to address the drug abuse problem as a way of decreasing depression-related suicides.(Correlation and Causation Essay-Example)
Amrhein, V., & Greenland, S. (2018). Remove, rather than redefine, statistical significance. Nature Human Behaviour, 2(1), 4-4.
Ganti, A. (2020). Correlation Coefficient. Corporate Finance & Accounting, 9, 145-152.
Gogtay, N. J., & Thatte, U. M. (2017). Principles of correlation analysis. Journal of the Association of Physicians of India, 65(3), 78-81.
Ioannidis, J. P. (2018). The proposal to lower P-value thresholds to. 005. Jama, 319(14), 1429-1430.
Walters, G. D., & Mandracchia, J. T. (2017). Testing criminological theory through causal mediation analysis: Current status and future directions. Journal of Criminal Justice, 49, 53-64.