What is the primary function of imputing synthetic values for missing data?

The primary function of imputing synthetic values for missing data is to address the incomplete case problem. When a dataset contains missing values, certain rows may be omitted from analysis or modeling, which can lead to a loss of valuable information and potentially bias the results. By imputing synthetic values, analysts can fill in these gaps in a way that maintains the integrity of the dataset and allows for a more comprehensive analysis.

The process of imputation enables the complete utilization of available data, providing insights that would otherwise be lost if only complete cases were considered. This inclusion of imputed data fosters better modeling and prediction capabilities, as it allows for a fuller representation of the underlying relationships in the data. The use of synthetic values ensures that the dataset remains robust, and enables analysts to work with a dataset that reflects more accurately the population being studied.