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Influencing Probability Judgments by Manipulating the Accessibility of Sample Spaces

Indiana University, mhanita{at}bcm.tmc.edu

Igor Gavanski

Indiana University

Russell H. Fazio

Indiana University

Two experiments are reported that evaluate predictions derived from the sample space framework of probability judgment, according to which both accuracy and error in judgments can be explained in terms of the sets of information, or sample spaces, on which they are based. Participants judged probabilities concerning fictitious individuals about whom a small amount of information was provided. The probability judgments took the form p(AIB), for which the set of B's delineates the appropriate sample space and the set of A's delineates the inappropriate sample space. The accessibility of the sets was manipulated by having participants explicitly describe typical instances from the applicable sets (Experiment 1) or by priming the relevant categories (Experiment 2). Results showed that judgments of conditional probabilities were more accurate when the appropriate set B was made accessible than when the in appropriate set A was made accessible.

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