This book explores how deep learning enhances statistical methods for hypothesis testing, point estimation, optimization, interpretation, and other aspects. It uniquely demonstrates leveraging deep learning to improve traditional statistical approaches, showcasing their superior performance in practical applications. Each topic includes essential background, clear method explanations, and detailed R code demonstrations through case studies. This allows readers to directly apply these methods to their own challenges and easily adapt the underlying principles to related problems. This book delves into statistical inference, introducing advanced strategies for hypothesis testing and point estimation. These innovative methods ingeniously combine both artificial and human intelligence, offering robust solutions for scenarios where traditional optimal analytical solutions are elusive or non-existent. A prime example of their real-world impact is in adaptive clinical trials, where these