StatLog

Missing data is an important issue for data analysis and can result from a variety of factors, including faulty collection, incomplete records, or simply a lack of data. Missing data can be particularly problematic when it is not randomly distributed throughout a dataset, because it can lead to bias and inaccurate conclusions. 

When dealing with missing data, one must first decide whether the missing data should be imputed or removed. Imputing data involves substituting values for missing data, while removing data involves deleting all records with missing data. The most appropriate approach will depend on the specific data and the purpose of the analysis. 

When imputing data, it is important to consider the potential sources of bias. For example, when imputing missing values, it is important to consider whether the imputed values are likely to be similar to the original values. Furthermore, imputation should be done in ways that minimize bias, such as through multiple imputation or using a regression model. 

In some cases, it may be appropriate to simply remove records with missing data. However, this approach should be used with caution, as it may result in a loss of information and lead to bias. 

Ultimately, when dealing with missing data, it is important to consider the context and purpose of the analysis to be aware of potential sources of bias, and to use the most appropriate method for imputing or removing data.