We discuss the main results in Estimation and Accuracy after Model Selection by Bradley Efron. This well written article, addresses how the variability in the model selection process can lead to unstable post-selection inferences. The main result is an easy to use, closed form formula for the standard deviation of a smoothed bootstrap (or bagged ) estimator. A projection type argument is given in the paper to prove that the proposed estimator is always less than or equal to the commonly used bootstrap standard error. We investigate the validity of these results on the prostate data set, a simulated data set where p > n, and the african data set as a representative example for GLM. We find substantial gains in accuracy of post selection inference confidence intervals for all subset selection, and modest gains when a regularization procedure is used for model selection.