Example Concept: To assess the impact of missing data, first conduct a missing data analysis to identify patterns and mechanisms. Use statistical tests like Little's MCAR test to determine if data is missing completely at random. Visualize missing data patterns using heatmaps or missingness matrices. Depending on the mechanism, consider imputation methods such as mean imputation, regression imputation, or advanced techniques like multiple imputation to handle missing data and mitigate its impact on your analysis.