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Related Concept Videos

Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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...
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...
Test for Homogeneity01:23

Test for Homogeneity

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 be stated as...
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...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.

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Infinium Assay for Large-scale SNP Genotyping Applications
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Stouffer's test in a large scale simultaneous hypothesis testing.

Sang Cheol Kim1, Seul Ji Lee, Won Jun Lee

  • 1Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea.

Plos One
|May 22, 2013
PubMed
Summary
This summary is machine-generated.

Stouffer's test, utilizing z-scores, offers a superior method for combining dependent test results in microarray analysis compared to Tippett's and Fisher's tests. This approach simplifies analysis by avoiding complex numerical procedures for determining null distributions.

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Area of Science:

  • Bioinformatics
  • Statistical genetics
  • Genomics

Background:

  • Combining dependent test results is crucial in microarray data analysis.
  • Existing methods like Tippett's and Fisher's tests lack known null distributions for dependent tests, requiring additional complex procedures.
  • Stouffer's test, based on z-scores, offers a potential alternative.

Purpose of the Study:

  • To re-evaluate Stouffer's test for its efficacy in analyzing large-scale microarray data with dependent partial test results.
  • To demonstrate the advantages of Stouffer's test over Tippett's and Fisher's omnibus tests.
  • To identify differentially expressed genes using Stouffer's test in a real-world microarray experiment.

Main Methods:

  • Revisiting Stouffer's test utilizing z-scores for combining dependent test results.
  • Numerical comparison of Stouffer's test against Tippett's and Fisher's omnibus tests regarding error rates.
  • Application of Stouffer's test to analyze microarray data for differentially expressed genes influenced by carcinogen compounds.

Main Results:

  • Stouffer's test yields a combined statistic with a normal distribution (mean 0), and its variance is directly estimable from experimental data.
  • Numerical comparisons indicated Stouffer's test exhibits lower error rates than Tippett's and Fisher's methods.
  • Real-world application successfully identified differentially expressed genes, confirming Stouffer's test superiority.

Conclusions:

  • Stouffer's test provides a statistically robust and computationally efficient method for combining dependent test results in microarray analysis.
  • It outperforms traditional p-value combination methods like Tippett's and Fisher's, especially when dealing with dependent tests.
  • The method is effective for identifying biologically relevant genes, such as those affected by genotoxic and non-genotoxic carcinogens.