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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

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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...
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P-value01:10

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P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
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Decision Making: Traditional Method01:14

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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.
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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.
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Testing a Claim about Population Proportion01:24

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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.
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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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On dependence assumption in p-value based multiple test procedures.

Jiangtao Gou1

  • 1Department of Mathematics and Statistics, Villanova University, Villanova, Pennsylvania, United States.

Journal of Biopharmaceutical Statistics
|January 6, 2023
PubMed
Summary
This summary is machine-generated.

Common multiple comparison procedures like Hochberg and Benjamini-Hochberg may inflate type I error rates. Stronger positive dependence is needed for guaranteed error control, not just independence or weak positive correlation.

Keywords:
Benjamini-Hochberg procedureDependence assumptionHochberg procedureMultiple testing procedureSimes test

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Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trials

Background:

  • Multiple comparison procedures (MCPs) are essential for multiplicity adjustment in clinical studies and research.
  • Commonly used MCPs include the Hochberg and Benjamini-Hochberg procedures.
  • A widespread misconception exists regarding the dependence conditions required for proper type I error rate control.

Conclusions:

  • The validity of type I error rate control for Hochberg and Benjamini-Hochberg procedures is highly sensitive to the dependence structure of test statistics.
  • Researchers must carefully consider and verify the assumed dependence conditions when applying these multiple testing methods.
  • Understanding these dependence conditions is crucial for accurate interpretation of results in confirmatory and exploratory research.