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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...

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Bonferroni Test

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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Sign Test for Matched Pairs

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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.
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Related Experiment Video

Updated: May 29, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Bayesian optimal discovery procedure for simultaneous significance testing.

Jing Cao¹, Xian-Jin Xie, Song Zhang

¹Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA. jcao@smu.edu

BMC Bioinformatics

|January 8, 2009

Summary

This study introduces a Bayesian Optimal Discovery Procedure (ODP) for high-throughput screening. The Bayesian ODP improves statistical power, especially with limited replicates, outperforming traditional methods.

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

High-throughput screening (HTS) involves tens of thousands of simultaneous tests, such as gene expression, drug sensitivity, and RNAi screening.
Limited replicate measurements (rarely exceeding 3) per test in HTS pose statistical challenges.
Shrinking variance estimates enhance the power of test statistics compared to traditional methods.

Purpose of the Study:

To develop a Bayesian hierarchical model incorporating variance shrinkage for improved statistical power in HTS.
To integrate this Bayesian model into the Optimal Discovery Procedure (ODP) for enhanced performance in multiple significance testing.

Main Methods:

A Bayesian hierarchical model with a mixture structure on variance components was proposed.
The model estimates were used within the Optimal Discovery Procedure (ODP).
Performance was evaluated using simulations (2-6 replicates) and two real biological datasets.

Main Results:

The Bayesian ODP demonstrated superior performance compared to competing methods, including the original ODP.
The advantage of the Bayesian ODP was particularly pronounced with a small number of replicates per test.
The model effectively borrows strength across genes for parameter estimation, enhancing accuracy.

Conclusions:

The proposed Bayesian ODP offers a powerful and computationally efficient approach for analyzing HTS data.
This method is especially beneficial when dealing with experiments with few replicates.
The study provides R code for implementing the Bayesian ODP, facilitating its application.