Utilizing the R programming language, you can easily conduct various types of t-tests to examine differences in sample means.

In this article, I’ll illustrate the application of 2 common t-tests:

- the independent two-sample t-test and the one-sample t-test.
- The independent two-sample t-test assesses whether the means of two independent groups differ significantly, while the one-sample t-test evaluates whether the mean of a single group significantly differs from a specified value.

### T-test Example 1

I can provide you with a basic example of how to perform t-tests in R and explain the code.

Let’s say you have two groups of data, and you want to compare the means of these groups to see if they are statistically different. You can use a t-test for this purpose.

Example code:

# Example data group1 <- c(10, 12, 14, 16, 18) group2 <- c(11, 13, 15, 17, 19) # Perform an independent two-sample t-test t_test_result <- t.test(group1, group2) # Print the result print(t_test_result)

Let me explain the above code in detail.

**Example Data**: We start by defining two groups of data, group1 and group2. Each group contains numeric values.**Perform t-test**: We use the t.test() function in R to perform a t-test. In this case, we’re performing an independent two-sample t-test, which is used when comparing the means of two independent groups.**Result**: The result of the t-test is stored in the variable t_test_result. This variable contains various statistics related to the t-test, including the t-statistic, the degrees of freedom, and the p-value.**Print Result**: We print the result of the t-test using the print() function.

The output will include the t-statistic, the degrees of freedom, the p-value, and whether the null hypothesis (that there is no difference between the means of the two groups) can be rejected at a certain significance level.

### T-test Example 2

Let me show you another example of performing a t-test in R, this time using a one-sample t-test.

# Example data group <- c(15, 16, 17, 18, 19) # Perform a one-sample t-test t_test_result <- t.test(group, mu = 20) # Print the result print(t_test_result)

I’ll explain the above code and it’s result.

**Example Data:**We have a single group of data called group, which contains numeric values.**Perform t-test:**Let’s use the t.test() function to perform a one-sample t-test. In this case, we’re testing whether the mean of group is significantly different from a specified value, in this case, 20. This is specified using the argument mu = 20.**Result:**The result of the t-test is stored in the variable t_test_result.**Print Result:**Print the result of the t-test using the print() function.

The output will include the t-statistic, the degrees of freedom, the p-value, and whether the null hypothesis (that the mean of group is equal to 20) can be rejected at a certain significance level.