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Related Concept Videos

Decision Making: P-value Method01:09

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-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
Testing a Claim about Population Proportion01:24

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.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Bonferroni Test01:10

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.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...

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Related Experiment Video

Updated: May 11, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

AAPL: Assessing Association between P-value Lists.

Tianwei Yu1, Yize Zhao, Shihao Shen

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.

Statistical Analysis and Data Mining
|April 30, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to assess associations between p-value lists from high-throughput data. The novel approach improves statistical power for detecting true associations, minimizing false positives.

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Published on: January 16, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Joint analysis of high-throughput datasets requires assessing associations between numerous p-value lists.
  • Existing methods like rank-based tests or contingency tables have limitations, including sensitivity to arbitrary thresholds and capturing unwanted technological biases.
  • Minimizing the influence of non-associated features is crucial for accurate analysis.

Purpose of the Study:

  • To develop a novel statistical method for evaluating the association between two long lists of p-values derived from high-throughput data.
  • To create an association score with a straightforward interpretation.
  • To enhance statistical power in detecting true associations while minimizing the impact of null features.

Main Methods:

  • Developed a new method based on feature-level concordance.
  • Utilized the local false discovery rate (fdr) to quantify concordance.
  • Compared the proposed method against existing approaches using simulations and real-world data.

Main Results:

  • The novel method demonstrated higher statistical power in simulations for detecting associations between p-value lists.
  • The proposed feature-level concordance approach provides a more robust assessment compared to rank-based tests or hard cutoff methods.
  • The method's utility was confirmed through a real data analysis.

Conclusions:

  • The developed method offers a powerful and interpretable approach for assessing associations between p-value lists in high-throughput data analysis.
  • This technique effectively addresses limitations of existing methods, leading to more reliable biological insights.
  • An R implementation is available for broader scientific application.