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Get exclusive insights into research trends and best practices from top experts! Access Voxco’s ‘State of Research Report 2024 edition’.
We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
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We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
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01
The T-test is an inferential statistic used to test differences between the means of two groups. A T-test is used when the data sets follow a normal distribution and may have unknown variances.
In the T-test, the data for grouping variables are categorical, while for the dependent variable is an interval scale.
T-tests allow you to test assumptions made about a population as a hypothesis-testing tool. It helps you to understand if there is any effect of the process on the population.
To establish the statistical significance, when calculating the T-test, you need to note the t-statistics, t-distribution, and the degree of freedom.
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02
A T-test is used when you want to compare the means between two groups. It cannot be of use when there are more than two groups. In such cases, you should use Analysis of Variance (ANOVA) or post-hoc tests.
As a parametric test of difference, a T-test also makes similar assumptions as another parametric test. T-test assumes that:
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03
Do the groups being compared come from a single population or from two different populations? Do you want to test the difference between the groups in a specific direction?
You need to ask yourself these questions before you can choose which T-test to use. Then, you can choose from the following types of T-tests:
Related: Paired vs Unpaired t-test: Comparison chart and examples
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04
To calculate T-test, you need three data values:
The formula of the Two-sample T-test is as follows:
T: T-value
x1 and x2: the difference between the means of the two groups
S2: pooled standard error of the two groups
n1 and n2: the number of data values in each group
When calculating the T-test, the outcome you get as a result is called T-value. This T-value is compared to a critical value table, called the T-Distribution Table. The comparison is made to determine the effect of chance on the difference and determine if the difference is outside that chance range.
The T-test is used to question if the difference between the groups represents a true difference or if it is a meaningless random difference.
You can also use Voxco’s T-test calculator to easily calculate T-test.
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05
There are two formats of a T-distribution table:
Used to assess a case that has a fixed value or range and a clear direction, positive or negative.
Used for range-bound analysis, for instance, questioning if the coordinates fall between -1 and +1.
You can use software like Voxco that supports fundamental statistical functions like MS Excel to calculate.
06
These are the outputs from the T-test.
06
The T-test is used to measure the difference between the means of groups divided by the pooled Standard Error of the means of two groups.
The calculation gives us an at-value that represents the magnitude of the difference between the means of two groups. Also, it determines if the difference exists purely by chance, i.e., p-value.
In case you have a big sample size, you can say that your T-value is significant if the value (absolute) is higher or equal to 1.96.
P-value: Probability Value, this tells you the likelihood of your data existing under the null hypothesis. It tells you the likelihood of seeing a test statistic as extreme or more extreme as the one calculated under a statistical test in case the null hypothesis is true.
For example, if your p-value is 0.5, this implies that 5% of the time, you may see a test statistic as extreme as the one found in case the null hypothesis is true.
The three types of T-tests are:
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