correlation is a statistical measure that is commonly used in expressing how two variables are linearly related. Correlation describes the simple relationship between variables without making a statement about cause and effect. Correlation refers to any statistical relationship, whether the relationship is casual or not. According to Schober et al. (2018), correlation is applied in the context of two continuous variables and is usually referred to as Pearson product-moment correlation. Pearson correlation coefficient is commonly used for data with a bivariate normal distribution. For ordinal data or for continuous data that is nonnormally distributed, spearman rank correlation is utilized to measure the monotonic association. The term is used to describe the degree to which variables move-in coordination. A positive correlation is when the two variables involved move in a similar direction. Negative correlation, on the other hand, refers to when two variables move in a different direction.
Causation implies that a change in one variable results in a change in the other variable. Causation describes a cause-effect relationship. Causation mainly has three conditions that include temporal precedence, covariation, and control for third variables. Correlation, however, does not equal or imply causation. A strong correlation can be interpreted as causality, but it can be due to other reasons such as random chance. Random chance is where variables may appear connected without any underlying relationship. A lurking variable may also result in variables appearing to have a strong relationship than the actual relationship. This is common in observational data where correlation does not confirm causation. It is, however, possible to infer causation from correlation through the use of directed acyclic graphs that provides the visual representation of the existing causal assumptions (Rohrer, 2018).
References
Rohrer, J. M. (2018). Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data. Advances in Methods and Practices in Psychological Science, 1(1), 27-42.
https://doi.org/10.1177%2F2515245917745629
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.
http://doi.org/10.1213/ANE.0000000000002864
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