# ttest - introduction

This post is part of 'Statistics | General' series

A t-test is a statistical test that is used to determine whether there is a significant difference in the means of two groups. There are different types of t-tests, such as the Student's t-test and the Welch's t-test, but they all use the t-distribution to make inferences about the means of the groups.

The most common t-test is the two-sample t-test, which is used to compare the means of two independent groups. The null hypothesis in this test is that there is no difference in means between the two groups, while the alternative hypothesis is that there is a difference in means between the two groups.

For example, imagine that you are a researcher and you want to know if a new teaching method is effective in improving student test scores. You randomly assign students to either a control group or an experimental group, and then measure their test scores. You want to compare the mean test scores of the two groups to see if the new teaching method had an effect.

To do this, you would use a two-sample t-test. The t-test calculates a t-value and a p-value, which tells you the probability that the observed difference in means is due to chance. If the p-value is low (usually less than 0.05), you can conclude that there is a statistically significant difference in means between the two groups, and thus that the new teaching method had an effect on test scores

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