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Types of Hypothesis Testing

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.

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...

Bonferroni Test

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...

Decision Making: Traditional Method

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...

Test for Homogeneity

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...

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...

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Related Experiment Video

Updated: Jun 21, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Testing the hypothesis of tissue selectivity: the intersection-union test and a Bayesian approach.

K Van Deun¹, H Hoijtink, L Thorrez

¹Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be

Bioinformatics (Oxford, England)

|August 13, 2009

Summary

A new Bayesian method effectively identifies selectively overexpressed genes, outperforming the traditional intersection-union test (IUT). This approach offers a more sensitive way to discover tissue-specific gene expression patterns.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Standard statistical tests are inadequate for identifying genes with preferential expression in specific tissues or conditions.
The intersection-union test (IUT) is a known method but is conservative and limited in the information it provides.
The IUT only considers the least differing tissue pairs, potentially missing broader expression patterns.

Purpose of the Study:

To develop a novel Bayesian procedure for quantifying evidence of selective gene over-expression.
To compare the performance of the proposed Bayesian method against the IUT in identifying selectively expressed genes.
To demonstrate the utility of the Bayesian method in analyzing real-world gene expression data.

Main Methods:

A Bayesian statistical procedure was developed to assess selective over-expression based on overall expression profiles.
A simulation study was conducted to evaluate the proposed method's performance against the IUT.
The Bayesian method was applied to publicly available gene expression data from 22 tissues.

Main Results:

The Bayesian procedure demonstrated superior performance compared to the IUT in identifying selectively expressed genes.
Application to real data revealed that the Bayesian method successfully selected genes with functions relevant to specific tissue characteristics.
The method is also capable of identifying underexpressed genes within a particular tissue.

Conclusions:

The proposed Bayesian method offers a more sensitive and informative approach for detecting selective gene over-expression than the IUT.
This method provides valuable insights into tissue-specific gene functions and expression patterns.
Software for implementing both the IUT and the Bayesian procedure is available for MATLAB and R.