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Related Concept Videos

P-value01:10

P-value

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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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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.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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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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Fisher's Exact Test01:08

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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...
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Test for Homogeneity01:23

Test for Homogeneity

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

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A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
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False discovery rate estimation for large-scale homogeneous discrete p-values.

Kun Liang1

  • 1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Biometrics
|October 23, 2015
PubMed
Summary

This study addresses challenges in genomics research with discrete p-values by proposing new methods for estimating the null proportion and false discovery rate (FDR). These novel conservative FDR estimators improve upon existing methods for high-throughput studies.

Keywords:
Discrete p-valuesDynamic adaptive methodsEmpirical processesFalse discovery rateMultiple testingSimultaneous inference

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-throughput genomics studies frequently generate large-scale discrete p-values.
  • Existing false discovery rate (FDR) methods often assume continuous p-values, posing challenges for discrete data.
  • Accurate estimation of the null proportion and FDR is crucial for reliable genomic analysis.

Purpose of the Study:

  • To develop and evaluate novel methods for estimating the null proportion and FDR specifically for discrete p-values.
  • To address the limitations of current FDR methodologies that assume continuous p-values.
  • To provide more accurate and conservative FDR estimation in high-throughput genomic studies.

Main Methods:

  • Proposed a novel class of conservative FDR estimators for discrete p-values in the finite sample setting.
  • Investigated the asymptotic properties of FDR estimators under weak dependence conditions.
  • Utilized simulation studies and a case study to compare the performance of the proposed method against existing approaches.

Main Results:

  • Introduced a new family of conservative FDR estimators tailored for discrete p-values.
  • Demonstrated the conservative nature of a broad class of FDR estimators in the asymptotic setting.
  • Showcased significant performance improvements of the proposed method over existing techniques through empirical evaluations.

Conclusions:

  • The proposed methods offer a significant advancement in handling discrete p-values for FDR control in genomics.
  • The novel estimators provide more reliable results compared to traditional methods when dealing with discrete p-values.
  • This work enhances the statistical toolkit for analyzing large-scale genomic datasets with discrete test statistics.