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Related Concept Videos

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...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
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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Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Estimating the proportion of true null hypotheses for multiple comparisons.

Hongmei Jiang1, R W Doerge

  • 1Department of Statistics, Northwestern University, Evanston, IL 60208, USA. hongmei@northwestern.edu

Cancer Informatics
|March 5, 2009
PubMed
Summary
This summary is machine-generated.

Accurate estimation of true null hypotheses is crucial for powerful genomic analyses. This study introduces a simple R method to estimate this proportion, improving statistical power in microarray studies.

Keywords:
epigenomicsfalse discovery ratemicroarraymultiple comparisonstype I error rate

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Whole genome microarray studies test millions of genomic features, necessitating robust statistical methods.
  • Traditional multiple comparison procedures like familywise error rate (FWER) are often too conservative for high-throughput data.
  • False discovery rate (FDR) procedures offer greater power but rely on accurate estimation of the proportion of true null hypotheses, which is often unknown.

Purpose of the Study:

  • To develop and validate an accessible, easy-to-implement method for estimating the proportion of true null hypotheses.
  • To provide R code for this estimation method to the scientific community.
  • To improve the statistical power of multiple comparison procedures in genomic studies.

Main Methods:

  • Proposed a novel, simple calculation for estimating the proportion of true null hypotheses.
  • Developed and made available R code implementing the proposed estimation method.
  • Compared the performance (bias and variance) of the proposed method against four existing procedures using simulated and real microarray data.

Main Results:

  • The proposed method demonstrated relatively small bias and small variance in estimating the proportion of true null hypotheses.
  • The R code for the estimation method is readily available for practical application.
  • The method's performance was validated through comparisons with existing procedures on both simulated and real genomic datasets.

Conclusions:

  • The developed method provides an accurate and accessible way to estimate the proportion of true null hypotheses.
  • This improved estimation enhances the reliability and power of FDR-controlled multiple comparison procedures in genomic research.
  • The method is broadly applicable to various multiple comparison scenarios beyond microarrays.