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Related Concept Videos

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.
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...
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...
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...
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.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

A robust method for large-scale multiple hypotheses testing.

Seungbong Han1, Adin-Cristian Andrei, Kam-Wah Tsui

  • 1Department of Statistics, University of Wisconsin-Madison, Medical Science Center 1300 University Avenue, Madison, WI 53706, USA.

Biometrical Journal. Biometrische Zeitschrift
|April 15, 2010
PubMed
Summary
This summary is machine-generated.

Accurate estimation of the proportion of true null hypotheses (pi(0)) is crucial for large-scale simultaneous inference. This study introduces a hierarchical Bayesian model offering a more stable and less biased pi(0) estimator, improving statistical analysis in genomics and imaging.

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12:59

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Published on: February 26, 2014

Area of Science:

  • Statistics
  • Genomics
  • Medical Imaging

Background:

  • Large-scale simultaneous inference, common in genomics and imaging, requires multiplicity adjustments to prevent inflated Type I errors.
  • Estimating the proportion of true null hypotheses (pi(0)) is critical, but existing methods often struggle with mid-range pi(0) values, yielding biased or unstable results.

Purpose of the Study:

  • To develop a more accurate and stable method for estimating pi(0) in large-scale hypothesis testing.
  • To address the limitations of current pi(0) estimation techniques that assume a large proportion of true null hypotheses.

Main Methods:

  • Proposes a hierarchical Bayesian model for estimating pi(0).
  • Evaluates the proposed method's performance through simulation studies, including scenarios with low-to-moderate correlation among test statistics.

Main Results:

  • The hierarchical Bayesian model provides a pi(0) estimator with significantly reduced bias and improved stability compared to existing methods.
  • The method demonstrates good performance even with correlated test statistics.

Conclusions:

  • The proposed hierarchical Bayesian model offers a robust solution for estimating pi(0) in large-scale simultaneous inference.
  • This approach enhances the reliability of statistical analyses in fields like genomics and medical imaging, as demonstrated by its application to a type II diabetes study.