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

Deductive Reasoning01:16

Deductive Reasoning

55.3K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
55.3K
Inductive Reasoning00:59

Inductive Reasoning

60.5K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
60.5K
Reasoning01:30

Reasoning

80
Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
80
Contingency Table01:29

Contingency Table

2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.5K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

330
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:
330
Causality in Epidemiology01:21

Causality in Epidemiology

436
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...
436

You might also read

Related Articles

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

Sort by
Same author

Extracellular matrix properties, interstitial flow, and VEGF gradients shape trophoblast behavior in a pumpless Trophoblast Invasion-on-Chip (TIoC).

Biomaterials·2026
Same author

A guide to using embedded ethics in human stem-cell-based embryo model research.

Nature cell biology·2026
Same author

Analyzing and supporting mental representations and strategies in solving Bayesian problems.

Frontiers in psychology·2026
Same author

Development of a p62 biodegrader for autophagy targeted degradation.

Nature communications·2025
Same author

The significance of hydrolase cascades on poly(aspartic) acid biodegradation assessment.

Chemosphere·2025
Same author

Aspects of zone-like identity and holotomographic tracking of human stem cell-derived liver sinusoidal endothelial cells.

Frontiers in cell and developmental biology·2025

Related Experiment Video

Updated: Jul 12, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

Measuring people's covariational reasoning in Bayesian situations.

Nicole Steib1, Stefan Krauss1, Karin Binder2

  • 1Mathematics Education, Faculty of Mathematics, University of Regensburg, Regensburg, Germany.

Frontiers in Psychology
|November 1, 2023
PubMed
Summary

This study explores covariational reasoning, focusing on how individuals understand the impact of base rates and error rates on positive predictive values. Findings indicate that conventional Bayesian reasoning skills may not be a prerequisite for this understanding.

Keywords:
Bayesian reasoningcovariational reasoningdouble-treenatural frequenciesunit square

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K
The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

5.8K

Related Experiment Videos

Last Updated: Jul 12, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K
The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

5.8K

Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Bayesian Statistics

Background:

  • Previous research on Bayesian reasoning has focused on assessing posterior probabilities from given priors.
  • Covariational reasoning, understanding how input probabilities affect output, has been less explored.

Purpose of the Study:

  • To systematically examine covariational reasoning in Bayesian inference.
  • To investigate the influence of changes in base rate, true-positive rate, and false-positive rate on positive predictive value.
  • To determine if conventional Bayesian reasoning skills are necessary for covariational reasoning.

Main Methods:

  • An empirical study was conducted with 229 university students.
  • Two methods for measuring covariational reasoning were compared: single-choice and slider formats.
  • Participants' understanding of the relationship between input probabilities and positive predictive value was assessed.

Main Results:

  • The study evaluated participants' ability to reason about the interplay of base rates and error rates in predictive values.
  • Different measurement formats (single-choice vs. slider) yielded insights into assessing covariational reasoning.
  • The relationship between conventional Bayesian reasoning and covariational reasoning was examined.

Conclusions:

  • Findings shed light on the distinct nature of covariational reasoning within Bayesian inference.
  • The study contributes to understanding how individuals process probabilistic information and its implications for decision-making.
  • Results inform educational approaches to teaching probabilistic and statistical reasoning.