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

The Two-Sample Wilcoxon Test is a non-parametric statistical method that is used to determine whether the medians of two different samples are different from each other. It is used to compare the medians 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 Wilcoxon Test, and one of them is by using the base R functions and packages such as “wilcox.test()” function and “ggpubr” package.

The “wilcox.test()” function is used to perform a Two-Sample Wilcoxon Test, it takes the data samples as input and returns the test statistic (W-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 medians of the two samples are different from each other.

Once the test has been done, the results can be visualized by using the “ggpubr” package. The ggpubr 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 ggboxplot() function to create box plots that shows the distribution of each sample data and the median of each sample. This can be a useful way to easily visualize the results of the Two-Sample Wilcoxon Test.

It’s worth noting that the wilcox.test() function is a powerful tool that allows you to use the Two-Sample Wilcoxon Test to understand the difference between two sample medians. However, you should keep in mind that the assumptions of the test must be met, such as the sample data should be continuous. It’s a good idea to consult with experts before using the wilcox.test() function, to make sure you are using the best suited method for your data.

In summary, the Two-Sample Wilcoxon Test is a non-parametric statistical method that is used to determine whether the medians of two different samples are different from each other. In R, there are several ways to perform a Two-Sample Wilcoxon Test, and one of them is by using the base R functions and packages such as “wilcox.test()” function and “ggpubr” package. The “wilcox.test()” function is used to perform a Two-Sample Wilcoxon Test, it takes the data samples as input and returns the test statistic (W-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 “ggpubr” package by creating box plots that shows the distribution of each sample data and the median of each sample. This can be a useful way to easily visualize the results of the Two-Sample Wilcoxon Test. It’s worth noting that the wilcox.test() function is a powerful tool that allows you to use the Two-Sample Wilcoxon Test to understand the difference between two sample medians. However, you should keep in mind that the assumptions of the test must be met, such as the sample data should be continuous. It’s a good idea to consult with experts before using the wilcox.test() function, to make sure you are using the best suited method for your data.

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

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

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