Whose statistical reasoning is improved by information about causal structure?

Simon McNair, Aidan Feeney

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (Journal of Experimental Psychology: General, 136, 430–450, 2007) proposed that a causal Bayesian framework accounts for peoples’ errors in Bayesian reasoning and showed that, by clarifying the causal relations among the pieces of evidence, judgments 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 judgments, but only for participants with sufficient understanding of basic concepts in probability and statistics.
Original languageEnglish
Pages (from-to)258-264
Issue number1
Early online date14 May 2014
Publication statusPublished - Feb 2015


Dive into the research topics of 'Whose statistical reasoning is improved by information about causal structure?'. Together they form a unique fingerprint.

Cite this