Abstract: Although recent attention has focused largely on improving predictive models, less consideration has been given to the prevalence of incorrect models selected by traditional statistical methods. In this work, the difficulty in choosing a scientifically correct model is quantified through theoretical and simulation work. Furthermore, the performance of traditional model selection techniques is compared with that of more recent methods. Specifically, when data sets contain a large number of explanatory variables, these results show that often the model with the highest R^2 (or adjusted R^2) or lowest AIC is not the scientifically correct model, suggesting that traditional model selection techniques be inappropriate. This work starts with the derivation of the probability of choosing the scientifically correct model in data sets as a function of regression model parameters when using R^2 or AIC. Next, simulation results show that these traditional model selection criteria are outperformed by methods that produce multiple candidate models for researchers' consideration. Potential extensions for dissertation topics will be discussed, along with a range of applications for this work!

**Please note: This talk has been tailored for students currently in the MS and PhD programs.