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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

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Published on: October 13, 2018

An open-source software program for performing Bonferroni and related corrections for multiple comparisons.

Kyle Lesack¹, Christopher Naugler

¹Faculty of Medicine, Bachelor of Health Sciences Program, Room G503, O'Brien Centre for the BHSc, 3330 Hospital Drive N.W. Calgary, Alberta T2N 4N1, 2.

Journal of Pathology Informatics

|January 26, 2012

Summary

This study addresses the problem of spurious findings in scientific literature due to multiple statistical comparisons. It introduces an open-source Python program for Bonferroni, Bonferroni-Holm, and Benjamini-Hochberg corrections, offering an accessible solution for researchers.

Keywords:

Bonferroni correction software program type I error

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Published on: November 27, 2019

Area of Science:

Statistics
Bioinformatics
Computational Biology

Background:

Multiple statistical comparisons in research can lead to an increased risk of type I errors, resulting in spurious findings.
Existing statistical correction methods like Bonferroni, Bonferroni-Holm, and Benjamini-Hochberg are crucial for maintaining scientific rigor.
A gap exists in accessible, open-source software for implementing these essential statistical corrections.

Purpose of the Study:

To present a novel, open-source software tool for performing common statistical corrections.
To provide researchers with an easy-to-use program for calculating family-wise significance and false discovery rates.
To enhance the reproducibility and reliability of scientific research by facilitating robust statistical analysis.

Main Methods:

Development of a program using Python 3.2.
Implementation of algorithms for standard Bonferroni correction.
Inclusion of calculations for the Bonferroni-Holm and Benjamini-Hochberg corrections.

Main Results:

A functional open-source program is now available for statistical correction calculations.
The program efficiently computes Bonferroni, Bonferroni-Holm, and Benjamini-Hochberg corrections.
This tool addresses the need for accessible, free software in statistical analysis.

Conclusions:

The developed Python program offers a practical solution for addressing multiple comparison issues in scientific research.
Widespread adoption of this open-source tool can help mitigate the reporting of spurious findings.
Researchers are encouraged to utilize this program to improve the statistical validity of their work.