What is the difference between Correlation and Causation?
Correlation and causation are two different concepts in statistics and research.
Correlation refers to a statistical relationship between two variables. It measures the degree to which two variables move together or vary together. In other words, correlation shows how changes in one variable are associated with changes in another variable. Correlation can be positive (both variables increase or decrease together) or negative (one variable increases while the other decreases). However, a correlation does not necessarily imply any cause-and-effect relationship.
Causation, on the other hand, is the relationship where one event (the cause) brings about another event (the effect). It implies that changes in one variable directly cause changes in another variable. Causation focuses on establishing a cause-and-effect relationship, providing evidence that one variable is responsible for the changes observed in another variable.
While correlation and causation are related, it is important to remember that correlation does not imply causation. A correlation simply indicates that two variables are related and tend to move together, but it does not prove that one variable causes the changes in the other. Establishing causation requires more rigorous research methods, such as experimental designs, control groups, and statistical tests, to establish a direct cause-and-effect relationship between variables.