The parameters of application scorecards are usually estimated using a sample that excludes rejected applicants which may prove biased when applied to all applicants. This paper uses a rare sample that includes those who would normally be rejected to examine the extent to which (1) the exclusion of rejected applicants undermines the predictive performance of a scorecard based only on accepted applicants, and (2) reject inference techniques can remedy the influence of this exclusion.