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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.
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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Wilcoxon Signed-Ranks Test for Matched Pairs01:09

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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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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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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Optimal Measurement Network of Pairwise Differences.

Huafeng Xu1

  • 1Silicon Therapeutics , Boston , Massachusetts 02210 , United States.

Journal of Chemical Information and Modeling
|October 16, 2019
PubMed
Summary
This summary is machine-generated.

Optimizing measurement networks significantly speeds up determining multiple quantities. This approach enhances computational predictions, like drug molecule binding free energies.

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Area of Science:

  • Statistics
  • Computational Chemistry
  • Network Analysis

Background:

  • Determining multiple quantities can be achieved by measuring individual values and pairwise differences.
  • These measurements form a network connecting quantities through their differences.

Purpose of the Study:

  • To analyze the optimization of measurement networks for determining multiple quantities.
  • To investigate A-optimal, E-optimal, and D-optimal network designs.

Main Methods:

  • Statistical analysis of simulated data.
  • Optimization of measurement/computational cost allocation.
  • Minimization of covariance matrix properties (trace, largest eigenvalue, determinant).

Main Results:

  • Optimal measurement networks substantially accelerate the determination of quantities.
  • Simulated data analysis confirms the efficiency of these networks.

Conclusions:

  • Optimal measurement networks offer a powerful tool for efficient quantity determination.
  • Potential applications include accelerating computational predictions in drug discovery.