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Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

Accuracy and Errors in Hypothesis Testing

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Updated: Feb 19, 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

EAMA: Empirically adjusted meta-analysis for large-scale simultaneous hypothesis testing in genomic experiments.

Sinjini Sikdar¹, Somnath Datta¹, Susmita Datta¹

¹Department of Biostatistics, University of Florida, Gainesville, Florida, United States of America.

|November 1, 2017

Summary

This summary is machine-generated.

This study introduces a novel meta-analysis method to accurately combine p-values from multiple genomic experiments. Our approach improves gene significance testing by empirically adjusting test statistics, reducing false discoveries in high-throughput genomic assays.

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Published on: July 27, 2021

Area of Science:

Genomics
Statistical Genetics
Bioinformatics

Background:

High-throughput genomic assays enable simultaneous testing of thousands of genes.
Standard statistical assumptions in large-scale multiple testing can lead to incorrect significance results and biased inference.
Combining results from independent genomic experiments risks gross false discoveries.

Purpose of the Study:

To develop a novel meta-analysis method for combining p-values from independent genomic experiments.
To address challenges in large-scale multiple testing frameworks and reduce false discoveries.
To improve the accuracy of identifying truly significant genes in genomic studies.

Main Methods:

Developed a meta-analysis method combining p-values from different independent experiments.
Employed empirical adjustments of individual test statistics and p-values.
Validated the method through simulation studies and real genomic datasets.

Main Results:

The proposed method outperforms standard meta-analysis approaches for significance testing.
Demonstrated accurate identification of truly significant gene sets.
Showcased the utility of empirical adjustments in large-scale genomic data analysis.

Conclusions:

The novel meta-analysis method provides a more accurate approach to combining results from multiple genomic experiments.
Empirical adjustments are crucial for reliable inference in large-scale multiple testing.
This method enhances the identification of significant genes, reducing false positives in genomic research.