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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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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...
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Identifying Statistically Significant Differences: The F-Test01:14

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The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
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Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Updated: Jun 8, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Directional false discovery rate control in large-scale multiple comparisons.

Wenjuan Liang1,2, Dongdong Xiang1, Yajun Mei3

  • 1KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China.

Journal of Applied Statistics
|November 7, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for identifying under-expressed and over-expressed genes. The procedure effectively controls false discoveries in both directions, improving gene expression analysis.

Keywords:
Gene expressiondata-drivenmarginal FDRmultiple testingseparate control

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Area of Science:

  • Biostatistics
  • Genomics
  • Bioinformatics

Background:

  • High-throughput technology generates massive gene expression data.
  • Identifying differentially expressed genes (under- and over-expressed) is crucial for disease research.
  • Existing methods often fail to control false discoveries separately for under- and over-expressed genes.

Purpose of the Study:

  • To develop a novel statistical procedure for multiple testing in gene expression analysis.
  • To separately control false discovery rates for under-expressed and over-expressed genes.
  • To maximize true discoveries while maintaining control over false positives in both directions.

Main Methods:

  • A three-classification multiple testing framework was employed.
  • A practical, data-driven procedure was developed.
  • The procedure was designed to control false discovery rates for under- and over-expressed genes distinctly.

Main Results:

  • The proposed procedure is theoretically valid and optimal.
  • It maximizes the expected number of true discoveries.
  • It simultaneously controls false discovery rates for both under-expressed and over-expressed genes with flexibility in nominal levels.
  • Effectiveness demonstrated on two large-scale genomic datasets.

Conclusions:

  • The developed procedure offers an effective solution for identifying directional gene expression changes.
  • It provides superior control over false discoveries compared to existing methods.
  • The flexibility and optimality make it a valuable tool for genomic data analysis.