Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.8K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.4K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.4K
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

1.3K
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
1.3K
Probability Laws01:49

Probability Laws

44.7K
Overview
44.7K
Correlation and Causation01:27

Correlation and Causation

43.5K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
43.5K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.0K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Knowledge through social networks: Accuracy, error, and polarisation.

PloS one·2024
Same author

How Communication Can Make Voters Choose Less Well.

Topics in cognitive science·2018
Same author

How Good Is Your Evidence and How Would You Know?

Topics in cognitive science·2018
Same author

On the ignorance of group-level effects-The tragedy of personnel evaluation?

Journal of experimental psychology. Applied·2018
Same author

Transitive reasoning distorts induction in causal chains.

Memory & cognition·2015
Same author

Category transfer in sequential causal learning: the unbroken mechanism hypothesis.

Cognitive science·2011

Related Experiment Video

Updated: Feb 28, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.5K

Betting on transitivity in probabilistic causal chains.

Dennis Hebbelmann1, Momme von Sydow2,3

  • 1Psychological Institute, University of Heidelberg, Hauptstr. 47-51, 69117, Heidelberg, Germany. Dennis.Hebbelmann@psychologie.uni-heidelberg.de.

Cognitive Processing
|June 15, 2017
PubMed
Summary

People often rely on transitive reasoning, assuming A leads to C through B, even when data shows A and C are unrelated. This study found participants continued to bet based on this flawed transitive assumption in experiments.

Keywords:
BettingCausal coherence hypothesisCausal inductionCausal reasoningTransitivity

Related Experiment Videos

Last Updated: Feb 28, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.5K

Area of Science:

  • Cognitive Science
  • Decision Making
  • Probabilistic Reasoning

Background:

  • Causal reasoning is essential for decision-making in probabilistic environments.
  • Reasoning can be based on observed covariation (correspondence) or inferred constraints (coherence) within causal models.
  • Transitivity is a common assumed constraint in causal chains, potentially leading to errors when empirical data contradicts it.

Purpose of the Study:

  • To investigate transitive reasoning in intransitive situations with negatively related distal events.
  • To examine how transitive reasoning influences betting behavior when it conflicts with empirical data.

Main Methods:

  • A sequential learning scenario was employed across three experiments.
  • Participants engaged in betting behavior within the learning environment.

Main Results:

  • Participants' betting behavior indicated a persistent influence of the transitivity assumption.
  • This influence was observed even when the provided data strongly contradicted the transitive inference.

Conclusions:

  • People may default to transitive reasoning, overriding contradictory empirical evidence.
  • This cognitive bias affects decision-making, specifically demonstrated through betting behavior in intransitive causal scenarios.