Selection bias is a central problem for causal inference in the social sciences.  Quite how central a problem it is, however, is often obscured by ambiguous terminology, needlessly technical presentations, and narrow rules of thumb. This paper uses directed acyclic graphs (DAGs)  to advance a precise yet intuitive global definition of endogenous selection bias and argue its theoretical and practical centrality for causal inference. The paper clarifies the fundamental structural difference between confounding and endogenous selection, shows that nearly all non-parametric identification problems relate to either confounding or endogenous selection, and argues that the problem of endogenous selection is indifferent to timing. Perhaps most importantly, we illustrate the importance of endogenous selection bias with numerous and varied examples from empirical social research.