The Dog that Didn’t Bark

doi:10.1017/9781009002042.009

Chapter 7 - The Dog that Didn’t Bark

Bayesian Approaches to Reasoning from Censored Data

from Part II - Sampling Mechanisms

Published online by Cambridge University Press: 01 June 2023

Brett K. Hayes ,

Saoirse Connor Desai ,

Keith Ransom and

Edited by

Peter Juslin and

Klaus Fiedler: Affiliation:
Universität Heidelberg
Peter Juslin: Affiliation:
Uppsala Universitet, Sweden
Jerker Denrell: Affiliation:
University of Warwick

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Summary

Inductive reasoning involves generalizing from samples of evidence to novel cases. Previous work in this field has focused on how sample contents guide the inductive process. This chapter reviews a more recent and complementary line of research that emphasizes the role of the sampling process in induction. In line with a Bayesian model of induction, beliefs about how a sample was generated are shown to have a profound effect on the inferences that people draw. This is first illustrated in research on beliefs about sampling intentions: was the sample generated to illustrate a concept or was it generated randomly? A related body of work examines the effects of sampling frames: beliefs about selection mechanisms that cause some instances to appear in a sample and others to be excluded. The chapter describes key empirical findings from these research programs and highlights emerging issues such as the effect of timing of information about sample generation (i.e., whether it comes before or after the observed sample) and individual differences in inductive reasoning. The concluding section examines how this work can be extended to more complex reasoning problems where observed data are subject to selection biases.

Keywords

reasoning induction inference Bayesian models sampling sampling assumptions selection bias

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Type: Chapter
Information: Sampling in Judgment and Decision Making , pp. 153 - 176

DOI: https://doi.org/10.1017/9781009002042.009 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2023

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