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

Confirmation Biases01:31

Confirmation Biases

8.5K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
8.5K
The Stanford Prison Experiment03:20

The Stanford Prison Experiment

24.9K
The famous and controversial Stanford Prison Experiment, conducted by social psychologist Philip Zimbardo and his colleagues at Stanford University, demonstrated the power of social roles, social norms, and scripts.
24.9K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.0K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.0K
Randomized Experiments01:13

Randomized Experiments

9.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.1K
Ethics in Research01:56

Ethics in Research

26.0K
Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.
26.0K
Social Proof00:52

Social Proof

32.5K
Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.
32.5K

You might also read

Related Articles

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

Sort by
Same author

Recognising and mitigating LLM Pollution in online behavioural research.

Nature communications·2026
Same author

A reporting checklist for large language models in behavioural science.

Nature human behaviour·2026
Same author

General scales unlock AI evaluation with explanatory and predictive power.

Nature·2026
Same author

Imagining and building wise machines: the centrality of AI metacognition.

Trends in cognitive sciences·2026
Same author

Detecting bias in algorithms used to disseminate information in social networks and mitigating it using multiobjective optimization.

PNAS nexus·2025
Same author

Delegation to artificial intelligence can increase dishonest behaviour.

Nature·2025
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Mar 2, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.5K

Validating Bayesian truth serum in large-scale online human experiments.

Morgan R Frank1, Manuel Cebrian1,2, Galen Pickard3

  • 1Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States of America.

Plos One
|May 12, 2017
PubMed
Summary
This summary is machine-generated.

Bayesian truth serum (BTS) effectively enhances honesty in large-scale online surveys. This method improves data quality even when true answer distributions are unknown, making it valuable for real-world applications.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K
Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
11:51

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

Published on: March 2, 2011

15.7K

Related Experiment Videos

Last Updated: Mar 2, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.5K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K
Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
11:51

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

Published on: March 2, 2011

15.7K

Area of Science:

  • Social Sciences
  • Behavioral Economics
  • Survey Methodology

Background:

  • Bayesian Truth Serum (BTS) is a method to improve honesty in surveys.
  • Existing research on BTS primarily uses small-scale experiments.
  • Large-scale online surveys are increasingly common, necessitating scalable methods.

Purpose of the Study:

  • To evaluate the effectiveness of Bayesian Truth Serum (BTS) in large-scale online surveys.
  • To determine if BTS improves data quality when the honest answer distribution is known.
  • To explore BTS's impact in scenarios where honest answers are not predetermined.

Main Methods:

  • Conducting large-scale online surveys utilizing the Bayesian Truth Serum (BTS) method.
  • Analyzing aggregated answer distributions against known honest distributions.
  • Applying BTS to a marketing survey with unknown honest answer distributions.

Main Results:

  • BTS significantly improved honesty in large-scale surveys with known honest answer distributions.
  • BTS treatment demonstrably altered answer distributions in a marketing application.
  • The study confirms BTS's utility beyond scenarios with pre-defined 'honest' answers.

Conclusions:

  • Bayesian Truth Serum (BTS) is a viable tool for enhancing honesty and data quality in large-scale online surveys.
  • BTS demonstrates efficacy in both controlled and exploratory survey environments.
  • The findings support the broader adoption of BTS in practical survey research and applications.