The study looked at how using Elastic Net penalized regression can introduce bias in model selection. To address this bias, the researchers introduced four correction methods. Through numerical experiments, they found that correcting the bias in the LASSO penalty term had a greater impact than correcting the bias in the Ridge penalty term. Based on this, they suggest a model selection approach that only corrects the bias in the LASSO penalty term.