Distribution shifts in the wild jeopardize the performance of machine learning models as they tend to pick up spurious correlations during training. Recent work (Nagarajan et al., 2020) has characterized two specific failure modes of out-of-distribution (OOD) generalization, and we extend this theoretical framework by interpreting existing algorithms as solutions to these failure modes. We then evaluate them on different image classification datasets, and in the process surface two issues that are central to existing robustness techniques. For those that rely on group annotations, we show how the group information in standard benchmark datasets is unable to fully capture the spurious correlations present. For those that don't require group annotations, the validation set utilized for model selection still carries assumptions that are not realistic in real-world settings, and we show how this choice of shifts in validation set could impact performance of different OOD algorithms.