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Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

P-value

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Bonferroni Test

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Related Experiment Video

Updated: Jul 7, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Efficient p-value estimation in massively parallel testing problems.

Rafal Kustra¹, Xiaofei Shi, Duncan J Murdoch

¹Department of Public Health Sciences, University of Toronto, Toronto, ON, Canada. r.kustra@utoronto.ca

Biostatistics (Oxford, England)

|February 29, 2008

Summary

This summary is machine-generated.

This study introduces a novel computational method for estimating numerous p-values, crucial for analyzing large genomic datasets and genetic interactions. The approach significantly reduces computation time compared to traditional permutation tests.

Keywords:

Bayesian testing Genome-wide association studies Interaction effects Permutation distribution Random Forest p-value distribution

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Area of Science:

Genomics
Statistical genetics
Computational biology

Background:

Modern genomic studies generate vast amounts of data, necessitating efficient statistical methods for hypothesis testing.
Evaluating billions of p-values, especially for interaction effects, poses significant computational challenges with traditional permutation tests.
Asymptotic distributions are not always readily available for complex test statistics.

Purpose of the Study:

To develop and evaluate a computationally efficient method for estimating a large number of p-values.
To address the limitations of permutation testing in large-scale genomic association studies, particularly for interaction effects.
To achieve substantial computational savings with minimal loss of accuracy.

Main Methods:

Constructing empirically derived null distributions for test statistics.
Employing a prediction model for initial p-value approximation.
Utilizing Bayesian methods to select a subset of p-values for refinement via permutation testing.

Main Results:

The proposed method demonstrates significant computational savings compared to full permutation testing.
The accuracy of the p-value estimates is minimally impacted by the computational efficiencies gained.
Successful application to a genome-wide case-control study of 2-way genetic marker interactions and colorectal cancer.

Conclusions:

The novel method offers a computationally feasible solution for large-scale p-value estimation in genomics.
This approach enables efficient analysis of complex genetic interactions, overcoming limitations of traditional methods.
The findings have implications for accelerating discovery in genetic association studies.