HOPE-Graph: A Hypothesis Evaluation Service considering News and Causality Knowledge

2021 ◽  
Author(s):  
Futoshi Iwama ◽  
Miki Enoki ◽  
Sachiko Yoshihama
Keyword(s):  
Hypothesis Evaluation

scholarly journals Teacher’s Reasons for Trust and Distrust in Scientific Evidence: Reflecting a “Smart But Evil” Stereotype?

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110285
Author(s):  
Tom Rosman ◽  
Samuel Merk
Keyword(s):  
Pilot Study ◽  
General Population ◽  
Educational Research ◽  
Scientific Evidence ◽  
Population Sample ◽  
Empirical Support ◽  
Evaluation Framework ◽  
General Population Sample ◽  
Online Study ◽  
Hypothesis Evaluation

We investigate in-service teachers’ reasons for trust and distrust in educational research compared to research in general. Building on previous research on a so-called “smart but evil” stereotype regarding educational researchers, three sets of confirmatory hypotheses were preregistered. First, we expected that teachers would emphasize expertise—as compared with benevolence and integrity—as a stronger reason for trust in educational researchers. Moreover, we expected that this pattern would not only apply to educational researchers, but that it would generalize to researchers in general. Furthermore, we hypothesized that the pattern could also be found in the general population. Following a pilot study aiming to establish the validity of our measures (German general population sample; N = 504), hypotheses were tested in an online study with N = 414 randomly sampled German in-service teachers. Using the Bayesian informative hypothesis evaluation framework, we found empirical support for five of our six preregistered hypotheses.

scholarly journals Multiple hypothesis evaluation in auditing

2002 ◽  
Vol 42 (3) ◽  
pp. 251-277 ◽  
Author(s):  
Rajendra P. Srivastava ◽  
Arnold Wright ◽  
Theodore J. Mock
Keyword(s):  
Multiple Hypothesis ◽  
Hypothesis Evaluation

A Test of a Two-Stage Model of the Evaluation of Hypotheses from Quantified First-Order Predicate Logic in Information Use Tasks

1992 ◽  
Vol 71 (3_suppl) ◽  
pp. 1091-1104 ◽  
Author(s):  
Peter E. Langford ◽  
Robert Hunting
Keyword(s):  
Predicate Logic ◽  
Information Use ◽  
Stage Model ◽  
Two Stage ◽  
First Order ◽  
Alternative Hypotheses ◽  
Age Range ◽  
Hypothesis Evaluation ◽  
Universe Of Discourse ◽  
First Order Predicate Logic

480 adolescents and young adults between the ages of 12 and 29 years participated in an experiment in which they were asked to evaluate hypotheses from quantified first-order predicate logic specifying that certain classes of event were necessarily, possibly, or certainly not included within a universe of discourse. Results were used to test a two-stage model of performance on hypothesis evaluation tasks that originated in work on the evaluation of conditionals. The two-stage model, unlike others available, successfully predicted the range of patterns of reply observed. In dealing with very simple hypotheses subjects in this age range tended not to make use of alternative hypotheses unless these were explicitly or implicitly suggested to them by the task. This tells against complexity of hypothesis as an explanation of the reluctance to use alternative hypotheses in evaluating standard conditionals.

An Examination of Different Performance Outcomes in an Analytical Procedures Task

2003 ◽  
Vol 22 (2) ◽  
pp. 219-235 ◽  
Author(s):  
Wendy J. Green ◽  
Ken T. Trotman
Keyword(s):  
Information Search ◽  
Performance Outcomes ◽  
Hypothesis Generation ◽  
Single Experiment ◽  
Search Patterns ◽  
Similar Information ◽  
Auditor Judgments ◽  
Analytical Procedures ◽  
Generation Information ◽  
Hypothesis Evaluation

In order to improve auditor judgments, it is first necessary to understand and evaluate what successful auditors do differently than those who are less successful. This study uses a computerized research instrument to examine in a single experiment the hypothesis generation, information search, hypothesis evaluation, and final judgment stages of the analytical procedures process. The inclusion of a criterion variable and the ability to search for additional evidence allows the study to examine in which of the various stages of analytical procedures auditors make less-than-optimal judgments. Of the 82 participants, 24 selected the correct cause, 19 never generated the correct cause as a hypothesis, and 39 generated the correct cause as a hypothesis but ended up not selecting it. The incorrect participants were divided into two categories: those who incorrectly selected the inherited hypothesis and those who incorrectly selected another self-generated non-error as the cause. The former group showed deficiencies in both information search and hypothesis evaluation compared to the correct group. The second incorrect group had similar information search patterns to the correct participants but had inferior hypothesis evaluation. These findings therefore lend support to the suggestion by Asare and Wright (2003) that not only is hypothesis generation important, but also information search and hypothesis evaluation are important.

Hypothesis Evaluation: A Reply to Hall

1983 ◽  
Vol 38 (138) ◽  
pp. 57 ◽  
Author(s):  
Thomas N. Huffman
Keyword(s):  
Hypothesis Evaluation

scholarly journals Explanation Supports Hypothesis Generation in Learning

2020 ◽  
Author(s):  
Erik Brockbank ◽  
Caren Walker
Keyword(s):  
Prior Knowledge ◽  
Learning Process ◽  
Category Learning ◽  
Large Body ◽  
Learning Task ◽  
Good Explanation ◽  
Hypothesis Generation ◽  
Cognitive Resources ◽  
Correct Hypothesis ◽  
Hypothesis Evaluation

A large body of research has shown that engaging in explanation improves learning across a range of tasks. The act of explaining has been proposed to draw attention and cognitive resources toward evidence that will support a good explanation—information that is broad, abstract, and consistent with prior knowledge—which in turn aids discovery and generalization. However, it remains unclear whether explanation acts on the learning process via improved hypothesis generation, increasing the probability that the correct hypothesis is considered in the first place, or hypothesis evaluation, the appraisal of the correct hypothesis in light of evidence. In the present study, we address this question by separating the hypothesis generation and evaluation processes in a novel category learning task and quantifying the effect of explanation on each process independently. We find that explanation supports the generation of broad and abstract hypotheses but has less effect on the evaluation of hypotheses.

scholarly journals A Review of Applications of the Bayes Factor in Psychological Research

2020 ◽  
Author(s):  
Daniel W. Heck ◽  
Udo Boehm ◽  
Florian Böing-Messing ◽  
Paul - Christian Bürkner ◽  
Koen Derks ◽  
...  
Keyword(s):  
Bayes Factor ◽  
Evidence Synthesis ◽  
Model Averaging ◽  
Psychological Research ◽  
Bayesian Variable Selection ◽  
Future Research ◽  
Steady Increase ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis ◽  
Hypothesis Evaluation

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The paper is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines.

scholarly journals Studying science denial with a complex problem-solving task

2021 ◽  
Author(s):  
Justin Sulik ◽  
Ryan McKay
Keyword(s):  
Problem Solving ◽  
Cognitive Style ◽  
Political Ideology ◽  
Data Gathering ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Multi Stage ◽  
Individual Cognition ◽  
Problem Solving Task ◽  
Hypothesis Evaluation

Explanations of science denial rooted in individual cognition tend to focus on general trait-like factors such as cognitive style, conspiracist ideation or delusional ideation. However, we argue that this focus typically glosses over the concrete, mechanistic elements of belief formation, such as hypothesis generation, data gathering, or hypothesis evaluation. We show, empirically, that such elements predict variance in science denial not accounted for by cognitive style, even after accounting for social factors such as political ideology. We conclude that a cognitive account of science denial would benefit from the study of complex (i.e., open-ended, multi-stage) problem solving that incorporates these mechanistic elements.

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