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Related Concept Videos

Test for Homogeneity01:23

Test for Homogeneity

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 be stated 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.
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Testing allele homogeneity: the problem of nested hypotheses.

Rafael Izbicki1, Victor Fossaluza, Ana Gabriela Hounie

  • 1Department of Statistics, Carnegie Mellon University, Pittsburgh, USA. rafaelizbicki@gmail.com

BMC Genetics
|November 27, 2012
PubMed
Summary

Traditional genetic association tests have limitations, especially when Hardy-Weinberg equilibrium is not met. A new Full Bayesian Significance Test offers a coherent and powerful alternative for evaluating genotypic and allelic homogeneity in genetic epidemiology.

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Area of Science:

  • Genetic Epidemiology
  • Statistical Genetics

Background:

  • Case-control studies are crucial for evaluating genotype-disease associations.
  • Traditional tests for allelic and genotypic homogeneity have limitations, particularly under deviations from Hardy-Weinberg equilibrium.
  • Existing methods may exhibit logical incoherence, where genotypic but not allelic homogeneity is accepted.

Purpose of the Study:

  • To identify flaws in traditional chi-squared tests for genotypic and allelic homogeneity.
  • To propose and evaluate an alternative frequentist approach for situations not adhering to Hardy-Weinberg equilibrium.
  • To introduce and validate the Full Bayesian Significance Test (FBST) for coherent assessment of genetic homogeneity.

Main Methods:

  • Critique of traditional chi-squared tests for homogeneity.
  • Development of a frequentist approach robust to Hardy-Weinberg disequilibrium.
  • Application of the Full Bayesian Significance Test (FBST) for genotypic and allelic homogeneity.
  • Power analysis comparing Bayesian and frequentist methods using real and simulated data.

Main Results:

  • Traditional tests can lead to logically impossible conclusions (e.g., accepting genotypic but rejecting allelic homogeneity).
  • Frequentist approaches, even when adapted, retain inherent incoherence issues.
  • The Full Bayesian Significance Test (FBST) provides a coherent framework, avoiding logical contradictions.
  • FBST demonstrates comparable statistical power to frequentist methods while ensuring coherence.

Conclusions:

  • The Full Bayesian Significance Test (FBST) offers a coherent and powerful alternative to traditional methods for association studies.
  • FBST simplifies the detection of genetic associations by providing logically consistent results.
  • This Bayesian approach enhances the reliability of genetic epidemiology findings.