What is T-Test?
The t-test is named after William Sealy Gosset’s Student’s t-distribution, created while he was writing under the pen name “Student.”
A t-test is a type of inferential statistic test used to determine if there is a significant difference between the means of two groups. It is often used when data is normally distributed and population variance is unknown.
The t-test is used in hypothesis testing to assess whether the observed difference between the means of the two groups is statistically significant or just due to random variation.
T-test
In statistics, various tests are used to compare different samples or groups and draw conclusions about populations. These tests, known as statistical tests, focus on analyzing the likelihood or probability of obtaining the observed data under specific assumptions or hypotheses. They provide a framework for assessing evidence in support of or against a particular hypothesis.
A statistical test begins by formulating a null hypothesis (H0) and an alternative hypothesis (Ha). The null hypothesis represents the default assumption, typically stating no effect or no difference, while the alternative hypothesis suggests a specific relationship or effect.
There are different statistical tests like Z-test, T-test, Chi-squared tests, ANOVA, Z-test, and F-test, etc. which are used to compute the p-value. In this article, we will learn about the T-test.
Table of Content
- What is T-Test?
- Assumptions in T-test
- Prerequisites for T-Test
- Types of T-tests
- One sample T-test
- Independent sample T-test
- Paired Two-sample T-test
- Frequently Asked Questions on T-Test