Running Head: Causal Structure in Probabilistic Judgement Whose Statistical Reasoning Is Facilitated by a Causal Structure Intervention? Population and Individual Differences in Bayesian Reasoning Cognitive Ability Using a 9-item Short Form Test of Raven's Standard Progressive Matrices

People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (2007) proposed that a causal Bayesian framework accounts for peoples’ errors in Bayesian reasoning, and showed that by clarifying the causal relations amongst the pieces of evidence, judgements on a classic statistical reasoning problem could be significantly improved. We aimed to understand whose statistical reasoning is facilitated by the causal structure intervention. In Experiment 1, although we observed causal facilitation effects overall, the effect was confined to participants high in numeracy. We did not find an overall facilitation effect in Experiment 2 but did replicate the earlier interaction between numerical ability and the presence or absence of causal content. This effect held when we controlled for general cognitive ability and thinking disposition. Our results suggest that clarifying causal structure facilitates Bayesian judgements, but only for participants with sufficient understanding of basic concepts in probability and statistics.