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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...
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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'SGoFicance Trace': assessing significance in high dimensional testing problems.

Jacobo de Uña-Alvarez1, Antonio Carvajal-Rodriguez

  • 1Departamento de Estadística e Investigación Operativa, Facultad de Ciencias Económicas y Empresariales, Universidad de Vigo, Vigo, Spain.

Plos One
|January 7, 2011
PubMed
Summary
This summary is machine-generated.

The Sequential Goodness-of-Fit (SGoF) method enhances high-dimensional testing by analyzing rejected null hypotheses. This study introduces the SGoFicance Trace for improved decision-making in multiple testing scenarios.

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • High-dimensional data analysis presents challenges in multiple hypothesis testing.
  • The Sequential Goodness-of-Fit (SGoF) method offers a novel approach to managing these problems.
  • Traditional methods may struggle with increasing numbers of tests, potentially missing true effects.

Purpose of the Study:

  • To enhance the SGoF method by allowing the significance level (γ) to vary across the interval (0,1).
  • To introduce the 'SGoFicance Trace' as a graphical tool to aid decision-making in multiple testing.
  • To provide an R script for computing the SGoFicance Trace, making the method accessible.

Main Methods:

  • The study extends the SGoF procedure by exploring a range of significance levels (γ) beyond the standard 0.05.
  • A graphical representation, the SGoFicance Trace, is developed to visualize significance across different γ values.
  • The SGoFicance Trace facilitates the identification of a robust set of significant results.

Main Results:

  • The SGoFicance Trace provides a more comprehensive view of significance compared to a single γ value.
  • The enhanced SGoF method demonstrates the ability to detect an increasing proportion of true effects as the number of tests grows.
  • The graphical tool aids in selecting an appropriate number of rejected hypotheses, balancing false positives and true discoveries.

Conclusions:

  • The SGoFicance Trace is a valuable graphical complement to the SGoF method for multiple testing.
  • Varying γ offers greater flexibility and potentially increased power in detecting true effects.
  • The developed R script enables practical application of the SGoFicance Trace in various high-dimensional data analyses.