This presentation will present the following perspective: When confronting a scientific problem, the field of statistics enters by viewing the problem as one where the scientific answer could be calculated if some missing data, hypothetical or real, were available. Thus, statistical effort should be devoted to three steps: 1. formulate the missing data that would allow this calculation, 2. stochastically fill in these missing data, and 3. do the calculations as if the filled-in data were available. This presentation discusses: conceptual benefits, such as causal inference using potential outcomes; computational benefits, such as the EM algorithm and related data augmentation methods based on MCMC; and inferential benefits, such as interval estimation and assessment of assumptions based on multiple imputation.