In
statistics,
imputation is the process of replacing
missing data with substituted values. When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation". Because missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with
listwise deletion of cases that have missing values. That is to say, when one or more values are missing for a case, most
statistical packages default to discarding any case that has a missing value, which may introduce
bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with an estimated value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data.