Teaching an Application of Bayes' Rule for Legal Decision-Making: Measuring the Strength of Evidence

Satake, Eiki; Murray, Amy Vashlishan

doi:10.1080/10691898.2014.11889692

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…For this reason, appropriately processing important information in a situation, such as a medical diagnosis test, is a crucial competence of professionals as well as laymen when confronted with epistemic uncertainty (e.g., Koller and Hoffrage, 2015). Similar judgments and decisions are also essential for lawyers, if evidence is given concerning a person being guilty or innocent (e.g., Satake and Murray, 2014), as well as in other professions (Hoffrage et al., 2015; Mellers et al., 2017). By contrast, a failure of processing information in a situation of epistemic uncertainty can lead to misjudgments and severe consequences (e.g., Stine, 1998; Schneps and Colmez, 2013).…”

Section: Introductionmentioning

confidence: 99%

How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations

Böcherer-Linder

Eichler

2019

Front. Psychol.

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Bayes’ formula is a fundamental statistical method for inference judgments in uncertain situations used by both laymen and professionals. However, since people often fail in situations where Bayes’ formula can be applied, how to improve their performance in Bayesian situations is a crucial question. We based our research on a widely accepted beneficial strategy in Bayesian situations, representing the statistical information in the form of natural frequencies. In addition to this numerical format, we used five visualizations: a 2 × 2-table, a unit square, an icon array, a tree diagram, and a double-tree diagram. In an experiment with 688 undergraduate students, we empirically investigated the effectiveness of three graphical properties of visualizations: area-proportionality, use of discrete and countable statistical entities, and graphical transparency of the nested-sets structure. We found no additional beneficial effect of area proportionality. In contrast, the representation of discrete objects seems to be beneficial. Furthermore, our results show a strong facilitating effect of making the nested-sets structure of a Bayesian situation graphically transparent. Our results contribute to answering the questions of how and why a visualization could facilitate judgment and decision making in situations of uncertainty.

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Section: Introductionmentioning

confidence: 99%

How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations

Böcherer-Linder

Eichler

2019

Front. Psychol.

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“…To understand the basic concept in Bayesian analysis, one could start with a historical overview of Bayes theorem. Satake and Murray (2014) have developed interesting methods for teaching Bayes theorem using a legal scenario. Next, the different approaches to calculating/estimating posterior distributions could be presented (e.g., grid estimation, quadratic estimation, and MCMC).…”

Section: Alternative Contentmentioning

confidence: 99%

Let’s Talk about Stats: Revising Our Approach to Teaching Statistics in Psychology

Reynolds

2021

Psychol Rep

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Undergraduate statistics in psychology is an important, often challenging, course for students. The focus in psychology tends to be on hypothesis tests, such as t tests and analysis of variance. While adequate for some questions, there are many other topics we might include that could improve that data analytic abilities of students and improve psychological science in the long run. Topics such as generalized linear modeling, multilevel modeling, Bayesian statistics, model building and comparison, and causal analysis, could be introduced in an undergraduate psychological statistics course. For each topic, I discuss their importance and provide sources for instructor’s continuing education. These topics would give students greater flexibility in analyzing data, allow them to conduct more meaningful analyses, allow them to understand more modern data analytic approaches, and potentially help the field of psychology in the long run, by being one part of the strategy to address the reproducibility problem.

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“…One reason for the importance of Bayes' rule is that it allows an update to probability on the basis of new evidence for decision making [3]. This decision making in so-called Bayesian situations (i.e., situations in which Bayes' rule can be applied) is decisive in various non-mathematical disciplines such as medicine [4], law [5] and economics [6]. However, research in psychology found consistently, over several decades, that people struggle greatly when dealing with Bayesian situations [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

How to Train Novices in Bayesian Reasoning

et al. 2022

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Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for evaluating situations of uncertainty. Bayesian Reasoning may be defined as the dealing with, and understanding of, Bayesian situations. This includes various aspects such as calculating a conditional probability (performance), assessing the effects of changes to the parameters of a formula on the result (covariation) and adequately interpreting and explaining the results of a formula (communication). Bayesian Reasoning is crucial in several non-mathematical disciplines such as medicine and law. However, even experts from these domains struggle to reason in a Bayesian manner. Therefore, it is desirable to develop a training course for this specific audience regarding the different aspects of Bayesian Reasoning. In this paper, we present an evidence-based development of such training courses by considering relevant prior research on successful strategies for Bayesian Reasoning (e.g., natural frequencies and adequate visualizations) and on the 4C/ID model as a promising instructional approach. The results of a formative evaluation are described, which show that students from the target audience (i.e., medicine or law) increased their Bayesian Reasoning skills and found taking part in the training courses to be relevant and fruitful for their professional expertise.

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Teaching an Application of Bayes' Rule for Legal Decision-Making: Measuring the Strength of Evidence

Cited by 6 publications

References 33 publications

How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations

How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations

Let’s Talk about Stats: Revising Our Approach to Teaching Statistics in Psychology

How to Train Novices in Bayesian Reasoning

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