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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...
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...
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...
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...
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...

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Related Experiment Video

Updated: Jun 5, 2026

A Cost Effective and Adaptable Scratch Migration Assay
08:59

A Cost Effective and Adaptable Scratch Migration Assay

Published on: June 30, 2020

Efficient p-value evaluation for resampling-based tests.

Kai Yu1, Faming Liang, Julia Ciampa

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA. yuka@mail.nih.gov

Biostatistics (Oxford, England)
|January 7, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient p-value evaluation method for resampling-based tests, significantly reducing computational intensity. The new procedure accelerates statistical hypothesis testing, making complex analyses like genetic association studies more feasible.

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Area of Science:

  • Statistics
  • Computational Biology
  • Genetics

Background:

  • Resampling-based tests are crucial for hypothesis testing when distributions are unknown.
  • These methods are computationally intensive due to repeated simulations for significance assessment.

Purpose of the Study:

  • To develop an efficient p-value evaluation procedure for resampling-based tests.
  • To significantly reduce the computational burden of these statistical tests.

Main Methods:

  • Adaptation of the stochastic approximation Markov chain Monte Carlo (MCMC) algorithm.
  • Development of a novel procedure for estimating p-values applicable to any resampling-based test.

Main Results:

  • The proposed procedure is 100-500,000 times more efficient than standard methods for small p-values.
  • Demonstrated feasibility for large-scale genetic association studies, such as prostate cancer research.

Conclusions:

  • The efficient p-value evaluation drastically reduces computational load for resampling-based tests.
  • This advancement makes versatile resampling-based tests computationally feasible for a broader range of applications.