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Related Experiment Video

Updated: Jun 24, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Properties of balanced permutations.

Lucinda K Southworth¹, Stuart K Kim, Art B Owen

¹Biomedical Informatics, Stanford University, Stanford, California 94305, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|April 14, 2009

Summary

This summary is machine-generated.

Balanced permutations, a bioinformatics method, lack proper null behavior due to no group structure. This leads to overly permissive p-values, inflating statistical significance in analyses.

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Area of Science:

Bioinformatics
Statistical Methods

Background:

Balanced permutations are a novel sample reuse technique applied in bioinformatics.
The statistical properties and reliability of this method are under examination.

Purpose of the Study:

To critically evaluate the null behavior of balanced permutation reference distributions.
To identify the underlying reasons for any observed deviations from correct statistical properties.

Main Methods:

Analysis of balanced permutation distributions under the null hypothesis.
Theoretical investigation into the group structure of permutation methods.

Main Results:

Balanced permutation distributions exhibit incorrect null behavior.
The absence of a group structure is identified as the cause of these deviations.
Permutation tests using this method can yield excessively permissive p-values.

Conclusions:

The balanced permutation method's statistical validity is compromised by its lack of group structure.
Researchers should exercise caution when interpreting p-values derived from this method, especially in bioinformatics applications.
Further development is needed to ensure the reliability of permutation-based statistical inference.