When does information about causal structure improve statistical reasoning?

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    When does information about causal structure improve statistical reasoning? / McNair, Simon; Feeney, Aidan.

    In: The Quarterly Journal of Experimental Psychology, Vol. 67, No. 4, 2014, p. 625-645.

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    @article{cc52fbb9753f4849b0a749743b90cf36,
    title = "When does information about causal structure improve statistical reasoning?",
    abstract = "Base rate neglect on the mammography problem can be overcome by explicitly presenting a causal basis for the typically vague false-positive statistic. One account of this causal facilitation effect is that people make probabilistic judgements over intuitive causal models parameterized with the evidence in the problem. Poorly defined or difficult-to-map evidence interferes with this process, leading to errors in statistical reasoning. To assess whether the construction of parameterized causal representations is an intuitive or deliberative process, in Experiment 1 we combined a secondary load paradigm with manipulations of the presence or absence of an alternative cause in typical statistical reasoning problems. We found limited effects of a secondary load, no evidence that information about an alternative cause improves statistical reasoning, but some evidence that it reduces base rate neglect errors. In Experiments 2 and 3 where we did not impose a load, we observed causal facilitation effects. The amount of Bayesian responding in the causal conditions was impervious to the presence of a load (Experiment 1) and to the precise statistical information that was presented (Experiment 3). However, we found less Bayesian responding in the causal condition than previously reported. We conclude with a discussion of the implications of our findings and the suggestion that there may be population effects in the accuracy of statistical reasoning.",
    author = "Simon McNair and Aidan Feeney",
    year = "2014",
    doi = "10.1080/17470218.2013.821709",
    language = "English",
    volume = "67",
    pages = "625--645",
    journal = "The Quarterly Journal of Experimental Psychology",
    issn = "1747-0218",
    publisher = "Psychology Press Ltd",
    number = "4",

    }

    RIS

    TY - JOUR

    T1 - When does information about causal structure improve statistical reasoning?

    AU - McNair, Simon

    AU - Feeney, Aidan

    PY - 2014

    Y1 - 2014

    N2 - Base rate neglect on the mammography problem can be overcome by explicitly presenting a causal basis for the typically vague false-positive statistic. One account of this causal facilitation effect is that people make probabilistic judgements over intuitive causal models parameterized with the evidence in the problem. Poorly defined or difficult-to-map evidence interferes with this process, leading to errors in statistical reasoning. To assess whether the construction of parameterized causal representations is an intuitive or deliberative process, in Experiment 1 we combined a secondary load paradigm with manipulations of the presence or absence of an alternative cause in typical statistical reasoning problems. We found limited effects of a secondary load, no evidence that information about an alternative cause improves statistical reasoning, but some evidence that it reduces base rate neglect errors. In Experiments 2 and 3 where we did not impose a load, we observed causal facilitation effects. The amount of Bayesian responding in the causal conditions was impervious to the presence of a load (Experiment 1) and to the precise statistical information that was presented (Experiment 3). However, we found less Bayesian responding in the causal condition than previously reported. We conclude with a discussion of the implications of our findings and the suggestion that there may be population effects in the accuracy of statistical reasoning.

    AB - Base rate neglect on the mammography problem can be overcome by explicitly presenting a causal basis for the typically vague false-positive statistic. One account of this causal facilitation effect is that people make probabilistic judgements over intuitive causal models parameterized with the evidence in the problem. Poorly defined or difficult-to-map evidence interferes with this process, leading to errors in statistical reasoning. To assess whether the construction of parameterized causal representations is an intuitive or deliberative process, in Experiment 1 we combined a secondary load paradigm with manipulations of the presence or absence of an alternative cause in typical statistical reasoning problems. We found limited effects of a secondary load, no evidence that information about an alternative cause improves statistical reasoning, but some evidence that it reduces base rate neglect errors. In Experiments 2 and 3 where we did not impose a load, we observed causal facilitation effects. The amount of Bayesian responding in the causal conditions was impervious to the presence of a load (Experiment 1) and to the precise statistical information that was presented (Experiment 3). However, we found less Bayesian responding in the causal condition than previously reported. We conclude with a discussion of the implications of our findings and the suggestion that there may be population effects in the accuracy of statistical reasoning.

    U2 - 10.1080/17470218.2013.821709

    DO - 10.1080/17470218.2013.821709

    M3 - Article

    VL - 67

    SP - 625

    EP - 645

    JO - The Quarterly Journal of Experimental Psychology

    T2 - The Quarterly Journal of Experimental Psychology

    JF - The Quarterly Journal of Experimental Psychology

    SN - 1747-0218

    IS - 4

    ER -

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