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Contingency Table

Contingency Table

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...

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Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Determination of Expected Frequency

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

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Related Experiment Video

Updated: Jun 8, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Detecting contingencies: an infomax approach.

Nicholas J Butko¹, Javier R Movellan

¹University of California, San Diego, United States. nbutko@ucsd.edu

Neural Networks : the Official Journal of the International Neural Network Society

|October 19, 2010

Summary

This summary is machine-generated.

Infant turn-taking behaviors optimize social information gathering. Simple reinforcement learning, using information gain as a reward, explains how infants learn social contingencies and develop social-emotional skills.

Related Experiment Videos

Last Updated: Jun 8, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Area of Science:

Computational developmental psychology
Robotics and artificial intelligence

Background:

Infant social-emotional development relies on detecting social contingencies.
Computational analysis offers insights into social development and robotic social behavior synthesis.

Purpose of the Study:

To investigate infant turn-taking behaviors in contingency detection.
To explain the acquisition of efficient contingency detection schemas using computational models.

Main Methods:

Analyzing infant turn-taking behaviors from a computational perspective.
Applying reinforcement learning mechanisms with information gain as a reward signal.

Main Results:

Infant turn-taking is tuned to optimally gather information about social responsiveness.
Reinforcement learning models successfully explain the acquisition of contingency detection schemas.
Learning is driven by self-reward for reducing uncertainty.

Conclusions:

Computational approaches, particularly reinforcement learning, can model infant social learning.
This research bridges computer science, engineering, and developmental psychology.
Stochastic optimal control offers a mathematical foundation for computational social behavior studies.