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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

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...
Bonferroni Test01:10

Bonferroni Test

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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
Cochran's Q Test01:17

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...

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Related Experiment Video

Updated: Jul 6, 2026

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

Methods for handling multiple testing.

Treva K Rice1, Nicholas J Schork, D C Rao

  • 1Division of Biostatistics and Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.

Advances in Genetics
|March 25, 2008
PubMed
Summary
This summary is machine-generated.

High-throughput genetic studies offer discovery potential but risk false positives. New methods are needed to balance false positives and negatives in large-scale genetic association testing.

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Last Updated: Jul 6, 2026

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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Published on: June 18, 2018

Area of Science:

  • Statistical genetics
  • Genetic epidemiology

Background:

  • High-throughput genotyping enables massive genetic association studies.
  • Testing numerous genetic loci increases the risk of false positive results.
  • Traditional multiple testing corrections like Bonferroni are inadequate for large-scale studies.

Purpose of the Study:

  • To review historical and current methods for addressing the multiple testing problem in genetic association studies.
  • To discuss balancing false positives and false negatives in large-scale genetic association analyses.

Main Methods:

  • Review of statistical methods for multiple testing correction.
  • Analysis of the impact of linkage disequilibrium on statistical power.
  • Examination of traditional versus modern approaches to genome-wide association studies.

Main Results:

  • Bonferroni correction is too conservative for studies with hundreds of thousands or millions of SNPs.
  • Failure to account for linkage disequilibrium leads to inflated false negatives.
  • Current methods struggle to balance statistical significance with biological relevance.

Conclusions:

  • A critical need exists for advanced statistical methods in genetic epidemiology.
  • Effective management of multiple testing is essential for accurate genetic association studies.
  • Future research should focus on developing robust approaches for large-scale genetic data analysis.