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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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Bonferroni Test01:10

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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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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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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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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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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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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Advances in p-Value Based Multiple Test Procedures.

Ajit C Tamhane1, Jiangtao Gou2

  • 1a Department of Industrial Engineering and Management Sciences , Northwestern University , Evanston , IL , USA.

Journal of Biopharmaceutical Statistics
|October 27, 2017
PubMed
Summary
This summary is machine-generated.

This review covers advances in p-value-based multiple test procedures (MTPs), including foundational Bonferroni and Simes tests, stepwise methods like Holm and Hochberg, and gatekeeping procedures for complex hypotheses. It also discusses graphical approaches for MTP visualization.

Keywords:
Adjusted p-valuesclosure methodfamilywise error rategatekeeping proceduresgraphical approachsimultaneous confidence intervalsstepwise procedures

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Area of Science:

  • Statistics
  • Statistical Methods

Background:

  • Multiple test procedures (MTPs) are crucial for controlling Type I error rates in complex statistical analyses.
  • Foundational methods like Bonferroni and Simes, along with their stepwise extensions, form the basis for many MTPs.

Purpose of the Study:

  • To review recent advancements in p-value-based multiple test procedures.
  • To provide a comprehensive overview of various MTPs, from basic to advanced gatekeeping methods.

Main Methods:

  • Review of established multiple testing procedures, including Bonferroni, Simes, Holm, Hochberg, and Hommel methods.
  • Discussion of stepwise MTPs for a priori ordered hypotheses.
  • Presentation of gatekeeping MTPs for hierarchically structured hypotheses with logical dependencies.
  • Overview of the graphical approach for constructing and visualizing MTPs.

Main Results:

  • The article details the evolution of MTPs, highlighting standard stepwise procedures and their improvements.
  • It introduces gatekeeping MTPs designed for complex hierarchical hypotheses.
  • The graphical approach is presented as a tool for MTP design and interpretation.

Conclusions:

  • The review synthesizes key developments in p-value-based MTPs.
  • It offers a structured understanding of various MTPs, aiding researchers in selecting appropriate methods.
  • The graphical approach provides a visual aid for complex MTPs.