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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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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...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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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...
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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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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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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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%...
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Test for Homogeneity01:23

Test for Homogeneity

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Multiple Comparison Tests01:13

Multiple Comparison Tests

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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...
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Multiple hypothesis testing in genomics.

Jelle J Goeman1, Aldo Solari

  • 1Biostatistics, Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.

Statistics in Medicine
|January 9, 2014
PubMed
Summary
This summary is machine-generated.

This overview explains multiple testing methods for genomics data, covering familywise error control and false discovery rate control. It guides users on selecting appropriate error rates and interpreting results in exploratory research.

Keywords:
BonferroniFDRfalse discovery proportionfalse discovery ratefamilywise error rate

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genomics research generates vast datasets requiring robust statistical methods.
  • Multiple testing is a critical challenge in analyzing high-dimensional genomics data.
  • Understanding error rates is essential for reliable interpretation of findings.

Purpose of the Study:

  • To provide a user-centric overview of multiple testing methodologies in genomics.
  • To clarify concepts and practical applications of familywise error rate and false discovery rate control.
  • To guide researchers in choosing appropriate statistical methods and interpreting results in exploratory genomics.

Main Methods:

  • Conceptual and practical descriptions of familywise error control methods.
  • Explanation of false discovery rate control and estimation techniques.
  • Discussion of assumptions and interpretation pitfalls for multiple testing procedures.

Main Results:

  • Detailed comparison of different error control strategies.
  • Guidance on selecting error rates based on experimental goals.
  • Consideration of gene selection strategies and validation in genomics studies.

Conclusions:

  • Effective multiple testing is crucial for valid genomics research.
  • Choosing the right error control method depends on the study's objectives.
  • Careful interpretation and validation are key to leveraging genomics data.