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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

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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Bonferroni Test01:10

Bonferroni Test

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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.
The null hypothesis of the...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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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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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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.
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: Apr 26, 2026

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry
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Compound p-value statistics for multiple testing procedures.

Joshua D Habiger1, Edsel A Peña2

  • 1Department of Statistics, Oklahoma State University, 301 MSCS building, Stillwater, OK, 74078, United States.

Journal of Multivariate Analysis
|August 1, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces compound p-value statistics, which use all available data for hypothesis testing. These new statistics improve the efficiency of multiple testing procedures, leading to more discoveries while maintaining error control.

Keywords:
Empirical BayesFalse discovery rateMicroarray analysisMultiple decision functionMultiple decision processMultiple testingSample splittingTest dataTraining data

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Area of Science:

  • Statistical methodology
  • Bioinformatics
  • Genomics

Background:

  • Traditional multiple testing procedures rely on p-values from individual tests.
  • These procedures assume independence and uniform distribution of p-values under null hypotheses.
  • Existing methods can be inefficient as p-values may not utilize all available data.

Purpose of the Study:

  • To develop tools for constructing compound p-value statistics.
  • To ensure these compound p-values remain independent and uniformly distributed under null hypotheses.
  • To enhance the efficiency of multiple testing procedures.

Main Methods:

  • Development of compound p-value statistics that incorporate all data.
  • Analytical derivations and simulations to validate properties of compound p-values.
  • Application of compound p-values to a real microarray dataset.

Main Results:

  • Compound p-value statistics are shown to depend on all available data.
  • Multiple testing procedures using compound p-values reject more false null hypotheses.
  • Type I error rate control is maintained with compound p-value statistics.

Conclusions:

  • Compound p-value statistics offer a more efficient approach to multiple hypothesis testing.
  • These statistics improve the power of statistical tests without compromising error rates.
  • Application to real data demonstrates enhanced discovery potential in genomics.