Correlations typically identify a relationship between two variables. Gathering information on how the variables relate, their strengths, and their outcomes show us their purpose to the experiment. Causations are a “Real-World” justification for why something has occurred. “It suggests that because x happened, y then follows; there is a cause and an effect.” (S, 2019) Distinguishing between two variables is the main objective in the experiments conducted. I think it is very important that these two are defined because they both have different operations. We use these concepts differently in various situations. Correlations are often used to understand the outcomes in an experiment, how the variables being studied/tested reacted leading to the results obtained. Causations can be used in an experimental platform as well. Identifying a “Real-World explanation” for why something occurred and how it aided in the experiment. In a business setting “ultimately, you want to be able to do is differentiate between the factors that actually did contribute to a more successful channel, the best part of the product, or the reason behind why customers are buying what you’re selling.” (S, 2019)
Reference:
S, M. (2019, December 21). Correlation vs Causation: What’s the Difference (Examples!). Retrieved September 11, 2020, from https://codingwithmax.com/correlation-vs-causation-examples/
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