Many psychological hypotheses require testing whether the similarity between two variables predicts important outcomes. For example, the ideal standards model posits that the match between (A) a participant's ideal partner preferences, and (B) the traits of a current/potential partner, predicts (C) evaluative outcomes (e.g., the decision to date someone, relationship satisfaction, breakup); tests of the predictive validity of ideal-matching require A × B → C analytic strategies. However, recent articles have incorrectly suggested that documenting a positive samplewide correlation between a participant's ideals and a current partner's traits (an A-B correlation) implies that participants pursued, selected, or desired partners with traits that matched their ideals. There are at least six alternative explanations for the emergence of a samplewide A-B correlation; A-B correlations do not provide evidence that ideals guide the selection/evaluation of specific partners. We review appropriately rigorous A × B → C tests that can aid scholars in identifying the circumstances in which ideal-matching exhibits predictive validity.