The Difference Between Causation and Correlation

Discussion of Statistics in Research
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The Difference Between Causation and Correlation
In research, a common phrase indicates that correlation does not imply causation (Rohrer, 2018). Causation and correlation are related concepts, but understanding their differences may assist the researcher in critically appraising sources and interpreting clinical research. According to Bhandari (2021), correlation is the association between two distinct variables such that a change in the independent variable shifts the dependent variable. In this respect, correlation can be used to measure the statistical relationship between dependent and independent variables. It implies that the variables shift concurrently. However, such concurrent change is not essentially associated with indirect or direct causal links. On the contrary, causation suggests that shifts in a specific variable trigger shifts within the other, thus indicating a cause-and-effect association. A causal connection exists between the variables because they are correlated with each other (Bhandari, 2021). In this vein, correlation does not imply causation, but causation typically implies correlation (Rohrer, 2018).
It is misleading to argue that correlation data demonstrate a cause-effect relationship due to problems that could arise when drawing credible clinical inferences from an inquiry. First, the third variable challenge relates to the potential for a confounding factor that influences dependent and independent parameters, creating a false impression that they are directly correlated. For instance, increased muggings and ice cream sales can be studied as directly correlated variables, although they do not posit a direct causal relationship. Government policy is a third parameter that could possibly influence these variables differently. When the researcher fails to acknowledge the third variable, the results and conclusions may be biased. Second, the directionality problem happens when two variables are correlated, with a potential for a causal association. However, it is difficult to identify the variable that prompts the other to change clearly. For instance, the level of vitamin D in the body directly correlates to depression. Still, it remains unclear whether vitamin D deficiency causes depression or whether depression causes a reduction in vitamin D consumption (Bhandari, 2021). Therefore, a feasible research design is necessary to differentiate between causal and correla...