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Observational astronomy is plagued with selection effects that must be takeninto account when interpreting data from astronomical surveys. Because of thephysical limitations of observing time and instrument sensitivity, datasets arerarely complete. However, determining specifically what is missing from anysample is not always straightforward. For example, there are always more faintobjects (such as galaxies) than bright ones in any brightness-limited sample,but faint objects may not be of the same kind as bright ones. Assuming they arecan lead to mischaracterizing the population of objects near the boundary ofwhat can be detected. Similarly, starting with nearby objects that can be wellobserved and assuming that objects much farther away (and sampled from ayounger universe) are of the same kind can lead us astray. Demographic modelsof galaxy populations can be used as inputs to observing system simulations tocreate ``mock'' catalogues that can be used to characterize and account formultiple, interacting selection effects. The use of simulations for thispurpose is common practice in astronomy, and blurs the line betweenobservations and simulations; the observational data cannot be interpretedindependent of the simulations. We will describe this methodology and arguethat astrophysicists have developed effective ways to establish the reliabilityof simulation-dependent observational programs. The reliability depends on howwell the physical and demographic properties of the simulated population can beconstrained through independent observations. We also identify a new challengeraised by the use of simulations, which we call the ``problem of uncomputedalternatives.'' Sometimes the simulations themselves create unintendedselection effects when the limits of what can be simulated lead astronomers toonly consider a limited space of alternative proposals.

What Is In a Survey? Simulation-Induced Selection Effects in Astronomy