Which method is not appropriate for not-applicable missing values?

The method identified as not appropriate for handling not-applicable missing values is one that relies on making predictions based on available data to fill in the missing values. This approach, known as estimation, generally assumes that the missing values can be inferred from the relationships and trends present in the existing dataset. However, this assumption may not hold for not-applicable values, as these missing values could signify that the data is not relevant or should not exist in that context. Consequently, estimating a value in such cases could introduce bias or misleading information into the analysis.

In contrast, synthetic methods might create artificial data points based on the patterns in other data, equal methods might replace missing values with a constant value such as the mean or median, and random methods could fill missing values with random selections from observed data points. These alternatives can be more suitable for other types of missing data as they do not attempt to infer meaning that may not exist, thus preserving the integrity of the dataset when faced with not-applicable situations.