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Related Concept Videos

P-value01:10

P-value

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 unlikely...
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...
Fisher's Exact Test01:08

Fisher's Exact Test

Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of the...
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...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...

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The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
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Estimation of false discovery rate using sequential permutation p-values.

Tim Bancroft1, Chuanlong Du, Dan Nettleton

  • 1Health Economics and Outcomes Research, OptumInsight, Eden Prairie, Minnesota 55344, USA. Timothy.Bancroft@optum.com

Biometrics
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PubMed
Summary
This summary is machine-generated.

This study introduces a sequential permutation method for testing multiple hypotheses. The approach efficiently estimates true null hypotheses and controls the false discovery rate (FDR) with reduced computational cost.

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Area of Science:

  • Statistics
  • Computational Biology
  • Bioinformatics

Background:

  • Multiple hypothesis testing is common in scientific research.
  • Traditional methods can be computationally intensive.
  • Estimating the false discovery rate (FDR) is crucial for interpreting results.

Purpose of the Study:

  • To develop a computationally efficient sequential permutation procedure for multiple hypothesis testing.
  • To enable estimation of the number of true null hypotheses (m0) and FDR.
  • To compare the performance of the new method against standard strategies.

Main Methods:

  • A sequential permutation procedure where the number of draws is a random variable.
  • Utilizing nonuniform, discrete null distributions for sequential p-values.
  • Estimating m0 and FDR from a collection of p-values.
  • Validation through real data analysis and simulation studies.

Main Results:

  • The proposed sequential approach yields results comparable to standard methods.
  • Significant reduction in computational expense compared to traditional strategies.
  • Effective estimation of m0 and FDR across various scenarios.

Conclusions:

  • The sequential permutation procedure offers an efficient alternative for multiple hypothesis testing.
  • This method provides a practical tool for controlling FDR in large-scale data analysis.
  • Reduced computational load makes complex analyses more accessible.