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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...
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.
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...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
What is a Hypothesis?01:14

What is a Hypothesis?

A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague statement. It...
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...

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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Global hypothesis testing for high-dimensional repeated measures outcomes.

Yueh-Yun Chi1, Matthew Gribbin, Yvonne Lamers

  • 1Department of Biostatistics, University of Florida, Gainesville, FL, USA. yychi@biostat.ufl.edu

Statistics in Medicine
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

New statistical tests handle high-dimensional

Related Experiment Videos

Last Updated: May 26, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

  • Biostatistics
  • Genomics
  • Proteomics
  • Metabolomics

Background:

  • High-throughput technologies generate high-dimensional, low-sample size data.
  • Traditional univariate approaches for Gaussian repeated measures have limitations with more variables than subjects.
  • Complex between-subject and within-subject designs pose analytical challenges.

Purpose of the Study:

  • To develop novel statistical tests for high-dimensional data common in omics studies.
  • To enable valid statistical inference when the number of variables exceeds the sample size.
  • To provide accurate analyses for complex experimental designs.

Main Methods:

  • Derivation of new statistical tests utilizing the dual of the error covariance matrix.
  • Ensuring statistical inference and computational efficiency through a nonsingular dual matrix.
  • Development of free software for broad applicability.

Main Results:

  • New tests accurately control Type I error rates.
  • Demonstrated reasonable statistical power even with few subjects and many variables.
  • Successful application to vitamin B6 deficiency metabolic study.

Conclusions:

  • The new methods provide accurate and efficient statistical analysis for high-dimensional omics data.
  • These methods are applicable to a wide range of complex experimental designs.
  • Free software is available for implementing these advanced statistical techniques.