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+---
+author_profile: false
+categories:
+- Statistics
+classes: wide
+date: '2019-12-31'
+excerpt: Let's examine why multiple imputation, despite being popular, may not be as robust or interpretable as it's often considered. Is there a better approach?
+header:
+  image: /assets/images/data_science_20.jpg
+  og_image: /assets/images/data_science_20.jpg
+  overlay_image: /assets/images/data_science_20.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_20.jpg
+  twitter_image: /assets/images/data_science_20.jpg
+keywords:
+- multiple imputation
+- missing data
+- single stochastic imputation
+- deterministic sensitivity analysis
+seo_description: Exploring the issues with multiple imputation and why single stochastic imputation with deterministic sensitivity analysis is a superior alternative.
+seo_title: 'The Case Against Multiple Imputation: An In-depth Look'
+seo_type: article
+summary: Multiple imputation is widely regarded as the gold standard for handling missing data, but it carries significant conceptual and interpretative challenges. We will explore its weaknesses and propose an alternative using single stochastic imputation and deterministic sensitivity analysis.
+tags:
+- Multiple Imputation
+- Missing Data
+- Data Imputation
+title: A Deep Dive into Why Multiple Imputation is Indefensible
+---
+