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Related Concept Videos

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...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
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.
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...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

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Related Experiment Video

Updated: May 14, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Multiple hypothesis testing: a methodological overview.

Anthony Almudevar1

  • 1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA. Antony_Almudevar@urmc.rochester.edu

Methods in Molecular Biology (Clifton, N.J.)
|February 7, 2013
PubMed
Summary
This summary is machine-generated.

Screening for differentially expressed genes involves multiple statistical hypothesis tests. This summary clarifies multiple testing methodology to help researchers select appropriate methods for gene expression analysis.

Related Experiment Videos

Last Updated: May 14, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray analysis for gene expression involves numerous statistical hypothesis tests.
  • Standard single hypothesis testing methods are insufficient for complex, large-scale analyses.
  • Identifying significant findings and quantifying associated errors are critical challenges.

Purpose of the Study:

  • To introduce current multiple testing methodologies.
  • To clarify the statistical issues inherent in large-scale hypothesis testing.
  • To provide a basis for selecting appropriate multiple testing methods.

Main Methods:

  • Review of established multiple testing procedures.
  • Discussion of statistical challenges in high-dimensional data.
  • Comparative overview of different methodological approaches.

Main Results:

  • Multiple testing introduces unique statistical complexities not present in single tests.
  • No single method is universally optimal for all multiple testing scenarios.
  • Understanding methodological nuances is key to accurate gene expression analysis.

Conclusions:

  • Effective gene expression screening requires careful consideration of multiple testing strategies.
  • Selecting the right method depends on the specific analytical goals and data characteristics.
  • This work aims to equip researchers with the knowledge to navigate complex statistical choices in genomics.