Mean imputation is most suitable when the proportion of missing data is small and the data are missing completely at random (MCAR). If the missingness is dependent on unobserved data or related to the missing values themselves, more sophisticated methods like multiple imputation or maximum likelihood estimation may be more appropriate.
