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Related Concept Videos

Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Probability Laws01:49

Probability Laws

Overview
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

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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

Assessing semantic coherence in conditional probability estimates.

Christopher R Fisher1, Christopher R Wolfe

  • 1Department of Psychology, Miami University, Oxford, OH 45056, USA.

Behavior Research Methods
|April 23, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method to evaluate semantic coherence in conditional probability estimates. The approach identifies five set relationships and three error types, improving probabilistic reasoning analysis.

Related Experiment Videos

Last Updated: Jun 2, 2026

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

Area of Science:

  • Cognitive Science
  • Decision Science
  • Probability Theory

Background:

  • Semantic coherence is crucial for evaluating probabilistic reasoning.
  • Existing methods for assessing coherence in conditional probability estimates are limited.
  • Higher-order coherence benchmarks are needed to analyze complex probability relationships.

Purpose of the Study:

  • To present an automated method for evaluating semantic coherence in conditional probability estimates.
  • To reduce the problem space of probability estimates into meaningful patterns.
  • To identify and categorize errors in probabilistic reasoning.

Main Methods:

  • Developed an automated evaluation method for semantic coherence.
  • Classified probability estimates into five patterns: identical, subset, mutually exclusive, overlapping, and independent sets.
  • Identified three theoretically significant nonfallacious errors in conditional probability judgments.

Main Results:

  • The automated method efficiently categorizes complex probability spaces.
  • The five identified patterns cover the spectrum of set relationships in probability.
  • Unique challenges in conditional probability, like division by zero and rounding errors, were addressed.

Conclusions:

  • The presented automated method offers a robust framework for assessing semantic coherence in conditional probabilities.
  • This approach enhances the analysis of probabilistic reasoning and decision-making.
  • The findings contribute to a deeper understanding of how humans and systems handle conditional probability estimates.