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Related Concept Videos

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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 Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
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...
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...
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.

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Optimal methods for using posterior probabilities in association testing.

Keli Liu1, Alexander Luedtke, Nathan Tintle

  • 1Department of Statistics, Harvard University, Cambridge, MA, USA.

Human Heredity
|April 4, 2013
PubMed
Summary
This summary is machine-generated.

The study establishes a theoretical foundation for using genotype dosage as an optimal one-dimensional summary statistic for imputation posterior probabilities. This finding enhances statistical power and computational efficiency in genetic association studies.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Haplotype imputation for unmeasured single nucleotide variants is increasingly utilized.
  • Genotype dosage is suggested by simulations to be optimal for imputation posterior probabilities, but lacks theoretical support.

Purpose of the Study:

  • To provide a theoretical foundation for using genotype dosage as a one-dimensional summary statistic.
  • To unify simulation findings on the optimality of genotype dosage.

Main Methods:

  • Analytical evaluation of dosage, mode, and other one-dimensional summary statistics.
  • Assessment of two-dimensional genotype posterior probability vectors.

Main Results:

  • The genotype dosage is proven to be an optimal one-dimensional summary statistic under a linear disease model.
  • This optimality is robust to violations of the linear disease model.
  • Simulation results corroborate the theoretical findings.

Conclusions:

  • The study provides a strong theoretical basis for employing genotype dosage in genetic association tests.
  • Genotype dosage is validated as a suitable summary statistic for genotype posterior probabilities across diverse genetic disease models.