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Impact of Missing Data on Selection Bias by Rohit Majumder is a document available to read on EtoBox.

What is Impact of Missing Data on Selection Bias about?

The document discusses how missing data handling can worsen selection bias, particularly when data is not random or when inappropriate methods like full case analysis or imputation are used. It outlines various techniques for handling missing data, such as deletion, imputation, and weighting, emphasizing the importance of selecting the right method to minimize bias. Additionally, the document explains outliers, their potential causes, and methods for detecting and dealing with them in datasets.

Author

Rohit Majumder

Language

EN