# How to analyse and visualise One-Sample-T-Test in R # How to analyse and visualise One-Sample-T-Test in R

The One-Sample T-Test is a statistical method that is used to determine whether a sample mean is different from a known population mean. It is used to compare the mean of a sample with a known value (hypothesized population mean). In R, there are several ways to perform a One-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 One-Sample T-Test, it takes the data sample as input and the hypothesized population mean 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 sample mean is different from the population mean.

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 the sample data, and you can add a vertical line at the hypothesized population mean. This can be a useful way to easily visualize the results of the One-Sample T-Test.

It’s worth noting that the t.test() function is a powerful tool that allows you to use the One-Sample T-Test to understand the difference between a sample mean and a population mean. 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 size 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 One-Sample T-Test is a statistical method that is used to determine whether a sample mean is different from a known population mean. In R, there are several ways to perform a One-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 One-Sample T-Test, it takes the data sample as input and the hypothesized population mean 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 the sample data, and add a vertical line at the hypothesized population mean. This can be a useful way to easily visualize the results of the One-Sample T-Test. It’s worth noting that the t.test() function is a powerful tool that allows you to use the One-Sample T-Test to understand the difference between a sample mean and a population mean. 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 size 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 One-Sample-T-Test in R.