Epidemiological studies over the past century have revealed many of the common primary risk factors for most major chronic diseases. A fundamental example was the elucidation of cigarette smoking as a risk factor for lung cancer. Nevertheless, for many diseases the mechanism, a biological understanding of how such risk factors lead to disease, remains elusive. Further, in the case of smoking, we are only too aware of individuals who do not develop lung cancer after a lifetime of heavy smoking and those who develop the disease with no apparent exposure to known risks. While these phenomena might be partially explained by the presence of as yet unidentified exposures, the possibility that there exist variables, perhaps genetic, that act synergistically or antagonistically with smoking is especially tantalizing. For example, is there a factor-some form of “immunity”—that protects certain smokers from cancer? Understanding and clarifying the roles of synergistic and antagonistic factors may be of particular value when developing a biological model for disease development or structuring targeted disease screening programs and interventions. For these reasons, questions surrounding synergism and antagonism are of considerable biological as well as epidemiological interest. At first glance, these issues seem to be accessible through the idea of statistical interaction, introduced in Chapter 8. However, in this chapter we discuss that while the ideas of synergism and antagonism appear self-evident, their relationship to statistical interaction is not as straightforward as we might have hoped.