Causation in statistics refers to a relationship between two events or variables where one event causes the other to occur. It is important to note that correlation does not imply causation, meaning that just because two variables are correlated, it does not necessarily mean that one causes the other. To establish causation, there must be a sequence in time from cause to effect, a plausible mechanism, and sometimes common and intermediate causes. Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. In order to determine causation, an appropriately designed experiment must be conducted where similar groups receive different treatments, and the outcomes of each group are studied. There are three possible relationships between two correlated events: direct causation, reverse causation, and common causation. It is important to understand the difference between correlation and causation, as recognizing their differences is crucial to understanding relationships between variables.