English
Occasion Segmentation Voxco is trusted by 450+ Global Brands in 40+ countries Download our Market Segmentation Guide to und ...
Independent Samples T-Test
SHARE THE ARTICLE ON
Table of Contents
What is a T-Test?
Before we can delve into understanding what the independent samples t-test is, let’s go over what a T-Test is and why it’s used.
A t-test is a statistical test that is used to compare the means of different datasets to identify the significant difference between them. In research, t-tests are generally used as a hypothesis testing tool that researchers use to test assumptions made on different populations.
There are three main types of t-tests, namely:
- Independent Samples T-Test: Used to compare the means of two groups
- Paired Sample T-Test: Used to compare the means of the same group at different periods of times
- One-Sample T-Test: Used to test the mean of a single group against a known mean
Within this article, we will specifically explore the independent samples t-test.
Transform your insight generation process
Create an actionable feedback collection process.
What is an Independent Samples T-Test?
The independent samples t-test, also known as the two-samples t-test or the unpaired samples t-test, is the most commonly used form of the t-test. It is used to compare the means of two sets of data. When the independent samples t-test is used to compare two samples from the same population, the means of both are generally identical. However, when used to compare samples taken from different populations, the means of the samples often differ. This test, therefore, helps researchers determine whether or not there is a difference in the mean of two samples, as well as the extent of the difference between them.
When is the Independent Samples T-Test Used
The independent samples t-test is used to test the statistical differences between the means of two groups. There are other tests that can also be used to do so, such as the z-test. The z-test is a statistical test used to compare the means of two data sets, however, unlike the t-test, it requires information about the population mean or standard deviation. This makes the t-test applicable to more situations as it doesn’t require as much information as the z-test does.
The t-test, therefore, is used in situations where:
- There are two independent samples
- Information on the population mean or standard deviation is not available
Assumptions for the Independent Samples T-Test
The independent samples t-test makes the following assumptions:
- Assumption of Independence: There must be two data sets that are not connected with each other, and are therefore independent.
- Assumption of Homogeneity of Variance: The variances of the dependent variable must be equal.
- Assumption of Normality: The sampling distribution of the mean across samples must be normal and therefore must fit the bell curve. The dependent variable must, therefore, be (approximately) normally distributed and must be measured on a continuous scale.
Download Market Research Toolkit
Get market research trends guide, Online Surveys guide, Agile Market Research Guide & 5 Market research Template
Null and Alternative Hypothesis in an Independent Samples T-Test
Generally, the null hypothesis for an independent samples t-test is that the two populations are equal. Therefore;
H0:u1=u2
However, researchers are normally trying to reject the null hypothesis and accept the alternative hypothesis, which states that the population means are not equal;
H0:u1u2
In order to reject or accept the alternative hypothesis, we must set a significance level known as alpha (). The value of alpha is usually set at 0.o5.
Your p-value indicates the probability that your null hypothesis is true. If your p-value is less than your selected alpha level, the null hypothesis is rejected and the alternative hypothesis is accepted. However, if your p-value is more than your selected alpha level, the null hypothesis is accepted and the alternative hypothesis is rejected.
See Voxco survey software in action with a Free demo.
FAQs on Independent Samples T-Test
An independent samples t-test is an inferential statistical test that is used to compare the means of two unrelated sets of data.
There are three main types of t-test used in hypothesis testing, namely;
- Independent samples t-test
- Paired sample t-test
- One sample t-test
An independent samples t-test is a statistical test that is used to compare the means of two different data sets.
The independent samples t-test is used when we want to compare the means of two different data sets, without having information on the population mean or standard deviation.
Explore all the survey question types
possible on Voxco
Read more
Customer Data Platform
Customer Data Platform SHARE THE ARTICLE ON Share on facebook Share on twitter Share on linkedin Table of Contents Wonder why after you search ...
WHAT IS MONTH OVER MONTH GROWTH RATE?
WHAT IS MONTH OVER MONTH GROWTH RATE? Exclusive Step by Step guide to Descriptive Research Get ready to uncover the how, when, what, and where questions in a r ...
Target Market
Target Market Voxco is trusted by 450+ Global Brands in 40+ countries See what question types are possible with a sample su ...
Tips to avoid Non-response Bias
Tips to avoid Non-response Bias BOOK A DEMO Voxco is trusted by 450+ Global Brands in 40+ countries See what question ty ...
CSAT survey questionnaire
CSAT survey questionnaire SHARE THE ARTICLE ON Share on facebook Share on twitter Share on linkedin Table of Contents Without customers, your b ...