Two results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, theta, is generally appropriate if and only if the missing data are "missing at random" and the observed data are "observed at random," and then such inference are gnerally conditional on the observed pattern of missing data. Second, ignoring the process that caused missing data when making Bayesian inferences about theta is generally appropriate if and only if the missing data are missing at random and the parameter of the missing date is "independent" of theta. Examples and discussion indicating the implications of these results are included. (21pp.)
