Which step is essential in handling missing data?

Question

BlackTom AI · Accepted Answer

When considering how to handle incomplete data, it helps to map out what the missingness looks like before choosing a strategy.
Option a: Ignoring the missing values ignores potentially important information and can bias results or reduce power; it is not a recommended first step.
Option b: Removing the dataset entirely if any data is missing is excessive and can discard valuable information, leading to biased samples and diminished analyses.
Option c: Identifying the pattern and extent of missing data is essential because knowing whether data are missing completely at random, at random, or not at random, and understanding the amount missing, guides appropriate handling like imputation or model-based methods.
Option d: Replacing all missing values with zero presumes that missingness equals zero, which can distort distributions, relationships, and introduce bias unless there is a justified reason for such imputation.

Data Science Principles (202504-LLecture)

Which step is essential in handling missing data?

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