Episode 16 — Handle missing data properly: MCAR, MAR, NMAR, and imputation implications
This episode teaches missing-data reasoning at a level that fits the exam, where the key skill is choosing a defensible handling method based on why data is missing, not just how much is missing. You’ll distinguish MCAR, MAR, and NMAR, and you’ll learn how each mechanism changes the risk of bias if you drop rows, fill values, or build models that implicitly treat “missing” as information. We’ll cover practical imputation options, from simple mean or median fills to model-based approaches, and we’ll emphasize the importance of fitting imputation only on training data to avoid leakage. You’ll also learn when missingness should become a feature, such as when “not provided” correlates with outcomes, and when that can backfire due to privacy, policy changes, or operational drift. Troubleshooting guidance will include checking missingness patterns, validating downstream metric impact, and documenting assumptions for auditability. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
