The presence of missing values in a data set can affect the conclusions made using the data. If, for example, several healthy participants dropped out of a longitudinal study and their data continued on as missing, the results of the study can be biased toward those unhealthy individuals who remained. Missing data values must not only be identified, they must also be understood before further analysis can be conducted.
The Explore Missing Values utility provides several ways to identify and understand the missing values in your data. It also provides methods for conducting multivariate normal imputation for missing values. These imputation methods assume that data are missing at random, which means that any differences between missing and non-missing data cannot be explained by the values of the other variables in the study. If you suspect that missing values are not nonmissing at random, then consider using the Informative Missing procedure, which is available in a number of platforms. See Informative Missing in Fitting Linear Models.
Caution: Be careful when analyzing data after imputing missing values, as the results have the potential to be biased.