Why can it be so hard to solve Bayesian problems? Moving from number comprehension to relational reasoning demands

Thinking and Reasoning 28 (4):605-624 (2022)
  Copy   BIBTEX


Over the last decades, understanding the sources of the difficulty of Bayesian problem solving has been an important research goal, with the effects of numerical format and individual numeracy being widely studied. However, the focus on the comprehension of probability numbers has overshadowed the relational reasoning demand of the Bayesian task. This is particularly the case when the statistical data are verbally described since the requested quantitative relation (posterior ratio) is misaligned with the presented ones (prior and likelihood ratios). In this regard, here I develop the proposal that research on Bayesian reasoning might improve by considering the notational alignment framework of mathematical problem-solving. Specifically, this framework can help to understand the sources of the main difficulties underlying Bayesian inferences based on verbal descriptions. In essence, the present proposal supports the general claim in math education regarding the need to foster relational comprehension to avoid misleading alignments and improve problem solving.



    Upload a copy of this work     Papers currently archived: 76,391

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Fragmentation and Old Evidence.Will Fleisher - forthcoming - Episteme:1-26.
Ramsey and the measurement of belief.Richard Bradley - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism.
Argument diagram extraction from evidential Bayesian networks.Jeroen Keppens - 2012 - Artificial Intelligence and Law 20 (2):109-143.


Added to PP

5 (#1,170,170)

6 months
5 (#154,856)

Historical graph of downloads
How can I increase my downloads?