"Don't select of the dependent variable" is a stock phrase of social science, whether one is talking about quantitative or qualitative analysis. In this paper (talk) we show how Pearl's theory of DAG's (directed acyclic graphs), particularly his notion of a collider variable, provides a very simple and general approach to understanding when selection or conditioning a variable fatally biases an analysis.  We start by presenting the basics of Pearl's theory. We contrast endogenous selection  to confounding. With endogenous selection, problems are created by conditioning on a variable. With confounding, problems are created by failure to condition on a variable.  We demonstrate that  some situations, unfortunately, are "damned if you do, damned if you don't". We then  analyze a number of classic examples with DAG's: traditional selection on the dependent variable, Heckman's endogeneity bias, ascertainment bias (Hernan), dependent censoring, instrumental variables, and the healthy worker effect.