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Related Concept Videos

Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
McNemar's Test01:23

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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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Related Experiment Video

Updated: Jun 7, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

A note on the tests for clustered matched-pair binary data.

Zhao Yang1, Xuezheng Sun, James W Hardin

  • 1Quintiles, Inc., Overland Park, KS 66211, USA. tonyyangsxz@gmail.com

Biometrical Journal. Biometrische Zeitschrift
|October 27, 2010
PubMed
Summary
This summary is machine-generated.

A new modified Obuchowski test offers improved power for clustered matched-pair data analysis. This statistical test is recommended for equal cluster sizes and large numbers of clusters with varying sizes.

Related Experiment Videos

Last Updated: Jun 7, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Biostatistics
  • Statistical Methods
  • Clinical Trials

Background:

  • McNemar's test is standard for analyzing matched-pair data but requires adjustments for clustered data.
  • Existing methods like Durkalski's and Obuchowski's tests handle clustered matched-pair data without distributional assumptions.
  • These methods are crucial in research where data naturally occurs in groups, such as clinical studies.

Purpose of the Study:

  • To propose a modified Obuchowski test for clustered matched-pair data.
  • To compare the performance of the modified Obuchowski test against existing Durkalski and Obuchowski tests.
  • To provide recommendations for test selection based on cluster size and variability.

Main Methods:

  • Development of a modified Obuchowski test statistic.
  • Extensive Monte Carlo simulation study to evaluate test performance (size and power).
  • Analysis of two real-world clustered matched-pair datasets.

Main Results:

  • The modified Obuchowski test demonstrates good nominal size and superior power compared to extant methods.
  • Obuchowski's original test is the most conservative.
  • Durkalski's test performance is intermediate, varying between the modified Obuchowski and original Obuchowski tests.

Conclusions:

  • The modified Obuchowski test is recommended for clustered matched-pair data with equal cluster sizes.
  • For varying cluster sizes, Durkalski's test is suitable for fewer clusters (K < 50), while the modified Obuchowski test is preferred for a larger number of clusters (K ≥ 50).
  • The proposed test offers a valuable alternative for analyzing complex clustered data in biostatistical applications.