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Related Concept Videos

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.
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...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
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...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

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Related Experiment Video

Updated: Jun 23, 2026

A Standardized Protocol for Preference Testing to Assess Fish Welfare
07:29

A Standardized Protocol for Preference Testing to Assess Fish Welfare

Published on: February 22, 2020

Weighted multiple hypothesis testing procedures.

Guolian Kang1, Keying Ye, Nianjun Liu

  • 1University of Alabama at Birmingham, Birmingham, AL 35294, USA. gkang@ms.soph.uab.edu

Statistical Applications in Genetics and Molecular Biology
|May 5, 2009
PubMed
Summary
This summary is machine-generated.

New statistical methods enhance genome research by improving power in multiple hypothesis testing. Weighted and generalized sequential procedures offer higher statistical power than traditional methods, controlling error rates effectively.

Related Experiment Videos

Last Updated: Jun 23, 2026

A Standardized Protocol for Preference Testing to Assess Fish Welfare
07:29

A Standardized Protocol for Preference Testing to Assess Fish Welfare

Published on: February 22, 2020

Area of Science:

  • Genomics
  • Statistical genetics
  • Bioinformatics

Background:

  • Multiple hypothesis testing is crucial in genome research.
  • Traditional methods like Bonferroni control error rates but lack power.
  • Weighted p-values can increase statistical power using prior information.

Purpose of the Study:

  • To develop novel statistical procedures for enhanced power in multiple hypothesis testing.
  • To introduce weighted and generalized sequential methods for genome data analysis.
  • To improve upon existing methods for controlling the family-wise error rate (FWER).

Main Methods:

  • Proposed a weighted Sidák procedure and estimated optimal weights.
  • Developed a generalized sequential (GS) Sidák procedure.
  • Incorporated optimal weights into GS procedures for maximum power.

Main Results:

  • Weighted Sidák procedure showed higher power than weighted Bonferroni.
  • GS Sidák procedure demonstrated greater power than GS Bonferroni.
  • Both GS procedures significantly outperformed weighted methods when using optimal weights.

Conclusions:

  • The proposed weighted and generalized sequential procedures offer increased statistical power.
  • These methods effectively control the family-wise error rate (FWER).
  • Optimal weight estimation is key to maximizing the performance of these new procedures in genome research.