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Data Imputation
Techniques used to replace missing data with substituted values, ensuring that datasets remain robust and useful for analysis.
Implications
The process of replacing missing or incomplete data with substituted values, often using statistical techniques, to maintain the integrity and usability of datasets, critical for accurate analysis and reporting.
Example
Example: A research firm uses data imputation to fill in missing survey responses, using the average of other responses to ensure that the analysis remains statistically valid.
Related Terms
Different from data interpolation, which estimates values within the range of existing data points, imputation typically addresses gaps in the dataset by substituting reasonable estimates.
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