Most articles on missing values assume the missing data are "missing at random" and ignore the process that "caused" the missing values. The condition under which this procedure is justified is explored here: the concept of missing at random is precisely defined, several examples are discussed, and two simple conditions are given which are sufficient to assure that the missing data are missing at random. (Author)