What type of methods uses a "one size fits all" approach to handle missing values?

The "one size fits all" approach to handling missing values refers to methods that apply a single strategy across all instances of missing data, regardless of the context or underlying patterns in the data. Synthetic Distribution is the correct answer because it typically involves generating synthetic data points for missing values based on the overall distribution of the available data, effectively treating the missing values uniformly.

This method simplifies the imputation process by applying the same distribution characteristics across the dataset, making it easier to implement but potentially less accurate if there are varying underlying patterns. As a result, it may not account for different reasons behind the missingness or the relationships between different features effectively.

In contrast, other methods such as Equal Distribution, Estimate Distribution, and Fixed Value Distribution may involve more nuanced or varied approaches to handling missing data, reflecting specific characteristics of the dataset or using estimates drawn from the observed values. These alternatives tend to be more tailored to the data at hand rather than a blanket strategy applied without consideration of context.