Which type of missing data refers to data that is missing at random?

The correct answer is the category known as "Missing at Random" (MAR). This term is used to describe a situation where the probability of a data point being missing is related to some observed data but not to the value of the missing data itself. In other words, when data is missing at random, the missingness can be explained by the available information.

For example, if survey responses are missing for certain demographics (like age or income), but this missingness can be predicted by other observed variables (like education level or geographic location), then the data is considered to be missing at random. Understanding this concept is crucial for applying appropriate statistical methods for handling missing data, as it informs the choice of imputation techniques or analyses that can still yield valid results.

In contrast, "Missing Completely at Random" (MCAR) refers to situations where the missing data is entirely independent of both observed and unobserved data, which is a more stringent condition and often leads to simpler analyses. "Not Missing at Random" (NMAR) indicates that the missingness is related to the unobserved data itself, posing more significant challenges in data analysis as it requires more complex models to address.

Pairwise deletion, while a method for handling missing data,