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Related Concept Videos

Bonferroni Test01:10

Bonferroni Test

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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.
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The null hypothesis of the...
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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.
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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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A complete procedure for testing a claim about a population proportion is provided here.
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Wilcoxon Signed-Ranks Test for Median of Single Population01:14

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The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Mixed directional false discovery rate control in multiple pairwise comparisons using weighted p-values.

Haibing Zhao1, Shyamal D Peddada, Xinping Cui

  • 1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China.

Biometrical Journal. Biometrische Zeitschrift
|November 21, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to control mixed directional false discovery rates (mdFDR) in large-scale comparisons, improving accuracy in identifying gene expression changes. The procedure offers greater power than existing methods for directional inferences.

Keywords:
Benjamini Hochberg procedureMicroarrayMixed directional false discovery rateMultiple pairwise comparisonWeighted p-value

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Genomics

Background:

  • Multivariate statistical analysis is crucial for comparing multiple experimental groups across numerous outcome variables, common in gene expression studies.
  • Identifying differentially expressed genes and their direction (up- or downregulation) is essential, but introduces directional errors (Type III errors) alongside traditional false positives and negatives.
  • Existing methods may not adequately control for these directional errors in high-dimensional data.

Purpose of the Study:

  • To introduce a novel procedure for controlling the mixed directional false discovery rate (mdFDR) at a specified level α.
  • To enhance the power of statistical tests in identifying true biological signals in large-scale multiple testing scenarios.
  • To provide a robust method for directional inferences in pairwise comparisons across multiple outcome variables.

Main Methods:

  • Development of a mixed directional false discovery rate (mdFDR) controlling procedure.
  • Utilization of weighted p-values, with weights defined as the inverse of twice the proportion of positive or negative discoveries.
  • Mathematical proof of the procedure's ability to control mdFDR at level α.

Main Results:

  • The proposed procedure mathematically controls the mdFDR at level α.
  • The method demonstrates greater statistical power compared to the GSP10 procedure.
  • Simulation studies and real data analysis confirm the outperformance of the proposed procedure over the GSP10 method.

Conclusions:

  • The new mdFDR controlling procedure offers improved accuracy and power for identifying differentially expressed genes with directional information.
  • This method is particularly beneficial for large-scale genomic studies requiring precise directional inferences.
  • The findings suggest a significant advancement in statistical methodologies for complex biological data analysis.