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Related Concept Videos

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...
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

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...
Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

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...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

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Related Experiment Video

Updated: May 29, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Significance testing in ridge regression for genetic data.

Erika Cule1, Paolo Vineis, Maria De Iorio

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. erika.cule05@imperial.ac.uk

BMC Bioinformatics
|September 21, 2011
PubMed
Summary

We developed a new statistical test for ridge regression coefficients, offering a computationally efficient alternative to permutation tests for analyzing large genetic datasets. This method aids in understanding the significance of genetic associations in high-dimensional data.

Related Experiment Videos

Last Updated: May 29, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Large-scale genetic association studies are feasible due to technological advancements.
  • High-dimensional genetic data (e.g., SNPs) exhibit high correlation, challenging standard regression techniques.
  • Penalized regression, like ridge regression, is increasingly used for high-dimensional data analysis.

Purpose of the Study:

  • To develop and evaluate a significance test for ridge regression coefficients.
  • To provide a computationally efficient alternative to existing methods.
  • To introduce a visualization tool for assessing coefficient significance across varying penalization levels.

Main Methods:

  • Development of a novel statistical test for ridge regression coefficients.
  • Simulation studies to evaluate test performance against permutation tests.
  • Introduction of the p-value trace for visualizing coefficient significance with increasing shrinkage.
  • Application to a lung cancer case-control dataset (EPIC).

Main Results:

  • The proposed test demonstrates comparable performance to permutation tests.
  • The new test offers significantly reduced computational cost.
  • The p-value trace effectively visualizes the impact of penalization on coefficient significance.
  • The method was successfully applied to real-world genetic data.

Conclusions:

  • The developed significance test is a valuable and computationally efficient alternative for analyzing ridge regression coefficients.
  • The p-value trace is an informative graphical tool for interpreting results, made feasible by the proposed test.
  • This approach enhances the analysis of high-dimensional genetic association studies.