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A t-test is a type of inferential statistical test that is used to determine the significant difference between the means of different groups of data. T-tests can be used to identify whether or not any differences in means could’ve happened by chance. T-tests are generally used with data that follows a normal distribution and possibly has unknown variances.
In research, the t-test is generally used as a hypothesis testing tool that can reject or accept the alternative hypothesis. There are many different hypothesis testing tools including the Student’s T-Test, F test, Chi-square test, analysis of variance (ANOVA), and more.
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A t-test allows us to compare the mean values of two populations, or data sets, so that we can determine whether or not they came from the same population. When doing the math, we take a sample from each set of data and create a problem statement that assumes a null hypothesis that both the means are equal. Once we’ve calculated the means and compared them against each other (or against a standard value), we can determine whether the null hypothesis should be accepted or rejected.
If both the means are not equal, the null hypothesis is rejected. This indicates that there is a significant difference between the means and therefore the difference in means is likely not due to chance. However, if the null hypothesis is accepted, the difference in means is not significant and are possibly due to chance.
There are three main types of t-test:
The t-test makes the following assumptions about the data:
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You require three key data values to calculate a t-test:
The t-test produces an outcome known as the t-value. This t-value is compared against a value obtained from the critical value table, also known as the T-Distribution Table. When compared, we can determine the effect of the chance alone on the difference, as well as whether the difference is outside or within the chance range.
The T-distribution table is available in two formats:
The t-test produces the following two outputs:
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A t-test is an inferential statistical test that is used to determine the significant difference between the means of different groups of data.
There are three main types of t-tests, namely;
In statistics, the t-score refers to the ratio between two groups and the differences within them. The higher the t-score, the more difference there is between the groups. The smaller the t-score, the more similarity there is between the groups.
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