# How to analyse and visualise Two-Samples-T-Test in R

The Two-Sample T-Test is a statistical method that is used to determine whether the means of two different samples are different from each other. It is used to compare the means of two samples and to determine if there is a significant difference between them. In R, there are several ways to perform a Two-Sample T-Test, and one of them is by using the base R functions and packages such as “t.test()” function and “ggplot2” package.

The “t.test()” function is used to perform a Two-Sample T-Test, it takes the data samples as input and returns the test statistic (t-value) and the p-value. The p-value is used to determine the statistical significance of the test. A p-value of less than 0.05 is considered to be statistically significant, which means that the means of the two samples are different from each other.

Once the test has been done, the results can be visualized by using the “ggplot2” package. The ggplot2 package is a powerful tool for creating beautiful and informative visualizations. It allows you to create a wide range of plots, such as scatter plots, line plots, bar plots, and histograms. You can use the geom_histogram() function to create a histogram that shows the distribution of each sample data, and you can add a vertical line at the mean of each sample. This can be a useful way to easily visualize the results of the Two-Sample T-Test.

It’s worth noting that the t.test() function is a powerful tool that allows you to use the Two-Sample T-Test to understand the difference between two sample means. However, you should keep in mind that the assumptions of the test must be met, such as the sample data should be normally distributed and the sample sizes should be greater than 30. It’s a good idea to consult with experts before using the t.test() function, to make sure you are using the best suited method for your data and check the assumptions for the test.

In summary, the Two-Sample T-Test is a statistical method that is used to determine whether the means of two different samples are different from each other. In R, there are several ways to perform a Two-Sample T-Test, and one of them is by using the base R functions and packages such as “t.test()” function and “ggplot2” package. The “t.test()” function is used to perform a Two-Sample T-Test, it takes the data samples as input and returns the test statistic (t-value) and the p-value. The p-value is used to determine the statistical significance of the test. Once the test has been done, the results can be visualized by using the “ggplot2” package by creating a histogram that shows the distribution of each sample data, and adding a vertical line at the mean of each sample. This can be a useful way to easily visualize the results of the Two-Sample T-Test. It’s worth noting that the t.test() function is a powerful tool that allows you to use the Two-Sample T-Test to understand the difference between two sample means. However, you should keep in mind that the assumptions of the test must be met, such as the sample data should be normally distributed and the sample sizes should be greater than 30. It’s a good idea to consult with experts before using the t.test() function, to make sure you are using the best suited method for your data and check the assumptions for the test.

In this Applied Machine Learning Recipe, you will learn: How to analyse and visualise Two-Samples-T-Test in R.

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