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Related Concept Videos

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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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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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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What is a Hypothesis?01:14

What is a Hypothesis?

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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...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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...
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Hypothesis testing at the extremes: fast and robust association for high-throughput data.

Yi-Hui Zhou1, Fred A Wright2

  • 1Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA yihui_zhou@ncsu.edu.

Biostatistics (Oxford, England)
|March 21, 2015
PubMed
Summary

This study introduces a fast and accurate approximation for exact association tests, improving hypothesis testing efficiency in high-throughput biomedical research. The method enhances power and provides reliable results, even with stringent thresholds and complex data structures.

Keywords:
Density approximationExact testingPermutation

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Genomics

Background:

  • Biomedical research often involves numerous hypothesis tests requiring strict significance thresholds.
  • Parametric association tests can yield inaccurate Type I errors at extreme thresholds, impacting reliability.
  • Exact permutation testing offers accuracy but is computationally demanding for high-throughput applications.

Purpose of the Study:

  • To develop a computationally efficient and accurate approximation for exact association tests of trend.
  • To provide a method that maintains accuracy at stringent thresholds and handles complex data structures.
  • To offer an alternative to computationally intensive exact tests for high-throughput biomedical analyses.

Main Methods:

  • An approximation to exact association tests of trend was developed, suitable for high-throughput settings.
  • The method was shown to be equivalent to likelihood ratio tests for generalized linear models (GLMs) under permutation.
  • Covariate handling was addressed for linear regression and stratified covariates in GLMs, mirroring exact conditional testing.

Main Results:

  • The proposed approximation is accurate and fast, enabling standard use in high-throughput settings.
  • The approach facilitates the provision of standard two-sided or doubled p-values.
  • Equivalence to likelihood ratio tests for common GLMs was demonstrated, confirming validity.

Conclusions:

  • The developed approximation offers a practical and efficient solution for association testing in high-throughput biomedical research.
  • This method enhances statistical power and maintains accuracy under stringent conditions.
  • The approach is broadly applicable, with an accompanying R package available for use.