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Related Concept Videos

Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Test Cross01:39

Test Cross

Alleles are different forms of the same gene. Humans and other diploid organisms inherit two alleles of every gene, one from each parent.
Test Cross01:39

Test Cross

Alleles are different forms of the same gene. Humans and other diploid organisms inherit two alleles of every gene, one from each parent.
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
Introduction to the Sign Test01:10

Introduction to the Sign Test

The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...

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Related Experiment Video

Updated: May 27, 2026

Making Sense of Listening: The IMAP Test Battery
11:25

Making Sense of Listening: The IMAP Test Battery

Published on: October 11, 2010

[Goodbye to the single sided test]

Jochen Hardt1

  • 1Medizinische Psychologie und Medizinische Soziologie, Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie Universitätsmedizin Mainz. jochen.hardt@gmx.de

Psychotherapie, Psychosomatik, Medizinische Psychologie
|November 15, 2011
PubMed
Summary

No abstract available in PubMed .

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