MCAR stands for “Missing Completely at Random,” which refers to a type of missing data mechanism in which the missingness of data is completely random and not related to any underlying factors or variables in the data set.
In other words, if data is missing completely at random, the likelihood of a data point being missing is completely independent of the observed or unobserved variables in the data set. It is a purely random phenomenon, and the missing values have no relationship with the rest of the data.
For example, suppose we are conducting a survey on a random sample of people, and some respondents did not answer a particular question. If the probability of missing the answer is the same for all respondents, regardless of their demographics or any other factors, the missing data would be considered MCAR.
When data are MCAR, the missing data can be safely ignored, and the complete cases can be analyzed using standard statistical techniques. However, if data is not MCAR, other methods are required to handle missing data, such as imputation or weighting techniques.