**Two-Sample t Test in R: Independent Groups (R Tutorial 4.2)**

In this video you will learn to conduct the independent two-sample t-test and confidence interval for the difference in means of two populations.

First, we will explain when it is appropriate to conduct the independent two-sample t-test and confidence interval for your data. Then you will see how to visually examine the relationship between the two variables in R before conducting the t-test and then will learn to conduct an independent two-sided t-test with non-equal population variances in R using the "**t.test**" command. Next, we will show you how to use the "**mu**" argument in two-sided t-test, the "**alt**" argument to do a one-sided t-test, the "**conf**" argument to change the confidence level for the t-test and the "**var.eq**" argument to assume equal population variances for t-test.

In this tutorial we will also demonstrate how you can let R know that groups are paired or dependent using the "**paired**" argument along with different ways for separating the groups in "**t.test**" command in R. Later in the tutorial, you will learn three different ways for deciding if you want to assume equal or non-equal variances: using boxplot, comparing the sample variances or conducting Levene's test using "**leveneTest**" command.

We recommend using the link below to download the dataset and practice on your own while watching this tutorial.

You can access and download the dataset that was used in this tutorial here: