How to analyse and visualise One-Sample-Wilcoxon-Test in R

The One-Sample Wilcoxon Test is a non-parametric statistical method that is used to determine whether a sample median is different from a known population median. It is used to compare the median of a sample with a known value (hypothesized population median). The Wilcoxon test is particularly useful when the data does not meet the assumptions of normality and when the sample size is small. In R, there are several ways to perform a One-Sample Wilcoxon Test, and one of them is by using the base R functions and packages such as “wilcox.test()” function and “ggplot2” package.

The “wilcox.test()” function is used to perform a One-Sample Wilcoxon Test, it takes the data sample as input and the hypothesized population median 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 sample median is different from the population median.

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 median. This can be a useful way to easily visualize the results of the One-Sample Wilcoxon Test.

It’s worth noting that the wilcox.test() function is a powerful tool that allows you to use the One-Sample Wilcoxon Test to understand the difference between a sample median and a population median. However, you should keep in mind that the assumptions of the test must be met, such as the 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 One-Sample Wilcoxon Test is a non-parametric statistical method that is used to determine whether a sample median is different from a known population median. It is particularly useful when the data does not meet the assumptions of normality and when the sample size is small. In R, there are several ways to perform a One-Sample Wilcoxon Test, and one of them is by using the base R functions and packages such as “wilcox.test()” function and “ggplot2” package. The “wilcox.test()” function is used to perform a One-Sample Wilcoxon Test, it takes the data sample as input and the hypothesized population median 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 “ggplot2” package by creating a histogram that shows the distribution of the sample data, and adding a vertical line at the hypothesized population median. This can be a useful way to easily visualize the results of the One-Sample Wilcoxon Test. It’s worth noting that the wilcox.test() function is a powerful tool that allows you to use the One-Sample Wilcoxon Test to understand the difference between a sample median and a population median. However, you should keep in mind that the assumptions of the test must be met, such as the 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 One-Sample-Wilcoxon-Test in R.