Missing Not at Random (MNAR) occurs when the probability of missing data is related to the unobserved data itself, meaning the missingness mechanism is dependent on the missing values. This makes it challenging to handle because standard methods like imputation or deletion can lead to biased analyses unless the missing data mechanism is explicitly modeled.