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Abstract Missing data are a common problem in statistics. Imputation, or filling in the missing values, is an intuitive and flexible way to address the resulting incomplete data sets. We focus on multiple imputation, which, when implemented correctly, can be a statistically valid strategy for handling missing data. The analysis of a multiply‐imputed data set is now relatively standard using readily available statistical software. The creation of multiply‐imputed data sets is more challenging than their analysis but still straightforward relative to other valid methods of handling missing data, and we discuss available software for doing so. Ad hoc methods, including using singly‐imputed data sets, almost always lead to invalid inferences and should be eschewed, especially when valid interval estimation or hypothesis testing is the objective. WIREs Comput Stat 2013, 5:20–29. doi: 10.1002/wics.1240 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Data Reduction, Smoothing, and Filtering Statistical and Graphical Methods of Data Analysis > Sampling

Monotone pattern of missingness.

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Illustrative display for type of pattern of missing data in NMES.

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Statistical and Graphical Methods of Data Analysis > Sampling
Statistical and Graphical Methods of Data Analysis > Data Reduction, Smoothing, and Filtering

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