Final answer:
Imputation by filling in category mean for missing values is a simple method that maintains dataset size and reduces bias from deletion, but it may reduce variability, introduce bias if data is not missing at random, and overlook complex variable relationships.
Step-by-step explanation:
When handling missing data in statistics, imputation is a common method used to fill in missing values. The approach of substituting missing values with the category mean has both advantages and disadvantages.
Advantages of Imputation with Category Mean
- Simplicity: This method is straightforward and easy to implement.
- Reduces bias compared to deleting rows with missing values.
- Maintains dataset size, which is crucial for maintaining statistical power in analyses.
Disadvantages of Imputation with Category Mean
- Can reduce variability in the dataset, which may lead to underestimating standard errors and confidence intervals.
- Potentially introduces bias if the data is not missing at random.
- May not fully represent the complexity of the data as it does not account for relationships between variables.