Multiple imputation refers to a method used in statistics to fill in missing data in a dataset. When data is missing, this technique creates multiple plausible values for the missing information, based on patterns in the available data. These plausible values are then used to simulate different completed datasets. By doing so, researchers can obtain more accurate and reliable estimates by accounting for the uncertainty caused by missing data.
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