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Inferred Statistics and Ecological Validity in Bayesian Reasoning

Arnold, Christopher B
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1521816469920848

Abstract Details

2018, Doctor of Philosophy (Ph.D.), Bowling Green State University, Psychology.
Research on Bayesian reasoning has indicated that people struggle with performance, usually providing non-normative responses. However, the problems used to assess Bayesian reasoning have been criticized on account of lacking ecological validity. In order to allow computation of a normative response, numerical information has been explicitly provided and responses have been shown to demonstrate base rate neglect. However, previous research has shown people to be highly attentive to base rates learned from experience (Nelson, Biernat, & Manis, 1990; Mastropasqua, Crupi, & Tentori, 2010). I hypothesize that performance is improved when the relationship between variables can be visualized. Although normative Bayesian judgment is determined using a mathematical formula, in most situations people make decisions without such explicit information and may develop effective heuristics for making likelihood judgments. The present study asked participants to make judgments using inferred base rate as well as inferred diagnostic information, a design which has not been used in any previous research. Because all of the statistics are inferred from experience it was expected that likelihood judgments would be made relatively intuitively and efficiently, avoiding base rate neglect and Bayesian conservativism. Performance on the inferred statistics problem was compared within-participants to performance on problems using natural frequencies and probabilities. Previous research has not compared performance in the same sample using inferred and explicit statistics. Contrary to my hypothesis, participants did not perform better when using inferred statistics than when using probabilities or natural frequencies. The present research implies that poor performance on Bayesian reasoning tasks is not the result of the way that information is presented or conflict between participants’ beliefs and provided statistics. Instead, it provides evidence that people are able to evaluate provided statistics in a way that is equivalent to judgments made using inferred statistics and that participants may use heuristics when provided with probabilities or natural frequencies. Although the results provided evidence for individual differences in Bayesian reasoning, numeracy did not predict performance.
Richard Anderson (Advisor)
Mary Hare (Committee Member)
Michael Zickar (Committee Member)
Tong Sun (Committee Member)
70 p.
Psychology
cognitive psychology; bayesian reasoning; heuristics; inferred statistics

Recommended Citations

Citations

  • Arnold, C. B. (2018). Inferred Statistics and Ecological Validity in Bayesian Reasoning [Doctoral dissertation, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1521816469920848

    APA Style (7th edition)

  • Arnold, Christopher. Inferred Statistics and Ecological Validity in Bayesian Reasoning. 2018. Bowling Green State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1521816469920848.

    MLA Style (8th edition)

  • Arnold, Christopher. "Inferred Statistics and Ecological Validity in Bayesian Reasoning." Doctoral dissertation, Bowling Green State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1521816469920848

    Chicago Manual of Style (17th edition)

bgsu1521816469920848
328
© 2018, all rights reserved.
This open access ETD is published by Bowling Green State University and OhioLINK.