This talk is dedicated to show how random matrix theory has been recently used to solve a few problems in high-dimensional statistics. The style will be pedagogical, descriptive and non-technical. A few important milestone results in the area will be presented together with their applications on hypothesis testing about large covariance matrices. Some unsolved and interesting problems (to author's opinion) would be also discussed.