Missing data is a pervasive issue in applied statistics, and this chapter offers a comprehensive treatment of its diagnosis and resolution. Beginning with a conceptual introduction, we discuss the mechanisms underlying missingness-MCAR, MAR, and MNAR-and their consequences for unbiased estimation. Later, the chapter provides practical tools for identifying patterns of missingness, including graphical and statistical diagnostics. It then reviews a spectrum of imputation methods, from simple techniques (mean substitution, regression imputation) to advanced algorithms (multiple imputation using chained equations and random forest imputation). Each method is evaluated in terms of bias, efficiency, and robustness, with detailed comparisons. Practical criteria for choosing an appropriate imputation strategy are also provided, followed by an in-depth discussion on ethical issues and transparency in reporting. Emerging trends in handling high-dimensional and MNAR data are also covered. Finally, the last section walks through a complete imputation example, ensuring readers can apply these concepts immediately.