As a junior Data Scientist, you developed a linear regression model using sklearn that showed a high R-square value, indicating a good fit based on the coefficient of determination. However, upon deployment, the model's predictions were significantly off. Your mentor attributed this to the Anscombe Quartet problem, which illustrates how datasets with similar statistical properties can have vastly different distributions. Beyond the Anscombe Quartet, what other critical issues in data analysis and model evaluation does this scenario highlight? Choose the two most relevant options.