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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...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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...
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...

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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

EPEPT: a web service for enhanced P-value estimation in permutation tests.

Theo A Knijnenburg1, Jake Lin, Hector Rovira

  • 1Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org

BMC Bioinformatics
|October 26, 2011
PubMed
Summary
This summary is machine-generated.

Computational biology permutation tests for statistical significance are computationally expensive. A new P-value estimator requires fewer permutations, enabling accurate estimation of small P-values efficiently.

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Area of Science:

  • Computational biology
  • Statistical significance testing

Background:

  • Permutation tests are standard for assessing statistical significance in computational biology.
  • Accurate estimation of small P-values requires computationally intensive permutations.
  • Existing methods are often infeasible due to high computational cost.

Purpose of the Study:

  • To present an alternative P-value estimator that reduces computational burden.
  • To provide a tool for reliable estimation of small P-values with fewer permutations.

Main Methods:

  • Development of a novel P-value estimation method.
  • Implementation of the estimator into a public website and web service (EPEPT).
  • Support for various common experiment types in computational biology.

Main Results:

  • The EPEPT tool offers programmatic access via a web service.
  • EPEPT accepts diverse computational biology experiment data.
  • Example clients in multiple programming languages are available for download.

Conclusions:

  • The EPEPT method is accessible and usable by biologists, bioinformaticians, and software engineers.
  • The tool simplifies the process of P-value estimation in computational biology.
  • EPEPT enhances the feasibility of statistical significance testing for small P-values.