Performing a paired t-test in R is one of the crucial requirements among statisticians. Such tests are useful when comparing two related groups of samples, meaning you have two values for the same samples. Unlike descriptive statistics that describe the sample one intends to measure, the t-test is more of an inferential statistic. Performing such tests can be challenging for any statistics student, even the experienced. As a result, most students seek alternative approaches to score big in their t-test assignments. Our Statistics Homework Help experts offer this comprehensive guide to make students understand statistics better. We will help with all your paired t-test and other statistics assignments.

**What is a Paired Samples T Test?**

The paired test is useful when one has two related samples. Such tests are valuable when checking whether a significant difference exists between two population means in matched data pairs. According to Kent State University, the paired measurements usually represent three key factors. These are the measurements at two different times, under two different conditions, and taken from two halves.

Besides, the paired-sample t-test is a parametric statistical method useful when there is one group of participants and data on two diverse conditions. For instance, one may need to gather data on stress or anxiety before and after exams. In such a case, one continuous dependent variable and one independent categorical variable exist.

**When should a paired samples t-test be used?**

The paired sample t-test should compare two population means for two correlating samples. In other words, one should use this statistical method when comparing similar subjects in both conditions.

**How to Do Paired T-Test in R**

When performing a paired t-test in R, one can use the built-in **t.test()** function with the following syntax:

**t.test**(x, y, paired = TRUE, alternative = “two.sided”)

- From the above syntax,
**X and Y**refer to the numeric vectors one intends to compare. - Paired refers to the logical value that specifies what one intends to compute in a paired t-test.
- The
**alternative**refers to the alternative hypothesis, which can be set to “two.sided” (default), “greater” or “less.”

Follow the following steps to perform the paired t-test using R studio:

After executing the following six steps in your paired t-test, let’s assume the following data is the results from your analysis:

**Reporting**

Based on the above data output, the paired t-test was performed to establish if a difference in exam exists before and after taking a statistical training program. The data results show a non-significant difference in the exam mark before taking the statistics training program, which is at (M = 3.46; SD = 1.36). Upon taking the training program, the results indicate (M = 3.63; SD = 1.04), t(98) = -0.85, p = 0.398.

Besides, these results indicate a mean difference of -1.16. The 95% confidence interval ranged from -0.54 to 0.22, which fails to show a significant difference between the sample means. These findings indicate no justification to reject the null hypothesis. Therefore, the typical conclusion is that there is no significant difference in exam marks before and after the statistics training program.

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## Let’s Help You in Statistics

When comparing two groups of samples, performing a paired t-test in R is essential for statisticians. This inferential statistic helps analyze differences in matched data pairs efficiently. Whether you need help performing a paired t-test, mean, predictive analysis, standard deviation, or exploratory data analysis, we have a team of statisticians for such tasks. Contact our statistics experts for help with your statistical assignments, whether paired t-tests, mean calculations, predictive analysis, standard deviation, or exploratory data analysis.