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Related Concept Videos

Confirmation Biases01:31

Confirmation Biases

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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?
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Hindsight Biases01:12

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Bias01:22

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Accuracy and Errors in Hypothesis Testing01:13

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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...
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Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Related Experiment Video

Updated: Sep 22, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Noise, Fake News, and Tenacious Bayesians.

Dorje C Brody1

  • 1Department of Mathematics, University of Surrey, Guildford, United Kingdom.

Frontiers in Psychology
|May 23, 2022
PubMed
Summary

This study introduces a signal processing model to analyze how information, including disinformation, affects decision-making dynamics. It reveals how confirmation bias can align with Bayesian updating and proposes using noise to combat fake news.

Keywords:
communication theoryconfirmation biasdisinformationelectoral competitionmarketingnoisesignal processing

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Area of Science:

  • Information dynamics
  • Decision-making theory
  • Signal processing

Background:

  • Understanding complex systems driven by information flow is crucial.
  • Existing models struggle to unify reliable information, noise, and disinformation.
  • Decision-making processes are susceptible to information control and biases.

Purpose of the Study:

  • To outline a signal processing-based modeling framework for information-driven systems.
  • To apply this framework to decision-making dynamics.
  • To quantify the impact of disinformation and explore novel strategies against fake news.

Main Methods:

  • Developed a unified framework representing reliable information, noise, and disinformation.
  • Input is the specification of information flow.
  • Utilized Bayesian logic to model decision-maker perception and updating.

Main Results:

  • Demonstrated a unified representation of different information types.
  • Quantified the impact of information control and disinformation.
  • Showed that confirmation bias is compatible with Bayesian updating.

Conclusions:

  • The proposed framework offers a new perspective on information dynamics in decision-making.
  • Confirmation bias can persist within Bayesian updating under specific conditions.
  • Leveraging noise, inspired by natural sciences, presents a potential strategy against disinformation and fake news.