Correlation is an association between two variables that tells us how linearly they are related and can change each other, and causality is when one variable changes another, AKA cause and effect (Dowd, 2017)

Correlation is an association between two variables that tells us how linearly they are related and can change each other, and causality is when one variable changes another, AKA cause and effect (Dowd, 2017). It is essential to make sure the differences are understood because they can directly affect a study and give false results when participants make changes that can affect the results. For example, In the Messina et al. study, a patient could have had low patient satisfaction, not necessarily because he was in a teaching or non-teaching hospital. It could have been because he had more complications during his admission that caused the patient to become frustrated. The frustration of having a longer hospital stay than anticipated can cause patient satisfaction to be affected.

Dowd, D. (2017, April 25). Difference Between Correlation and Causality. Retrieved September 11, 2020, from https://sciencing.com/difference-between-correlation-causality-8308909.html

Singh, S. (2018, August 24). Why correlation does not imply causation? Retrieved September 11, 2020, from https://towardsdatascience.com/why-correlation-does-not-imply-causation-5b99790df07e