Summary: A broad review is given of various aspects of interaction. After a brief discussion of the relation between absence of interaction and constancy of variance, the main part of the paper considers interaction in the usual sense, connected with the structure of an expected value in multiply classified data. Interactions are first classified depending on the character of the factors involved. Techniques for detecting interactions are reviewed, including aspects of model formulation, especially for unbalanced data. Special methods for interpreting interactions are described. Finally, the relation between interactions and error estimation is considered and some short remarks made on extensions to binary and other essentially nonnormal models.