When is it appropriate to replace missing values with the mode?

Replacing missing values with the mode is particularly appropriate for categorical variables. The mode represents the most frequently occurring value in a dataset, making it a logical choice for filling in missing entries where the data is categorical. Since categorical variables represent distinct groups or categories, employing the mode helps maintain the integrity of these groups and avoids introducing biases that could arise from filling in values with means or medians, which are more suited for numerical data.

In the context of handling missing data, categorical variables often have non-numeric values that cannot be meaningfully averaged or summed, emphasizing the importance of using an approach that respects the categorical nature of the data. Utilizing the mode allows for a simple yet effective way to retain the most common category while addressing missingness.