How to perform Dunn's test in Python?
Dunn's test is a statistical technique for comparing the means of several samples. When it's required to compare the means of numerous samples to identify which ones are noticeably different from one another, Dunn's test is frequently employed in a range of disciplines, including biology, psychology, and education. We shall examine Dunn's test in−depth in this article, along with a python implementation.
What is Dunn's Test?
Dunn's test is a statistical analysis method used to compare the means of multiple samples. It is a multiple comparison test method used to compare the means of more than two samples to determine which samples are significantly different from each other.
When the normality assumption is violated, Dunn's nonparametric Kruskal−Wallis test is sometimes used to compare the means of multiple samples. If there were significant differences between sample means, the Kruskal−Wallis test was used to find these differences. Make pairwise comparisons of sample means to determine which samples are significantly different from each other. Then use Dunn's test to compare the sample means.
Performing Dunn's test in Python
To run Dunn's test in Python, we can use the posthoc dunn() method of the scikit-posthocs library.
The following code demonstrates how to use this function -
grammar
sp.posthoc_dunn(data, p_adjust = 'bonferroni')
Bartlett's test statistic and p-value are returned after this function receives a data array
parameter
p_adjust is a p value adjustment method
To demonstrate testing in Python, consider the following scenario: A researcher wishes to discover whether three different fertilizers cause different levels of plant growth. They randomly selected 30 different plants and divided them into three groups of ten plants, each using a different fertilizer. They measured the height of each plant at the end of a month.
algorithm
Install scikit-posthocs library
Specify the growth data of 10 plants by group
Merge all 3 combinations into one data
Dunn's test for p-values using Bonferonni correction
Example
Running Dunn's tests using the scikit-posthocs library is demonstrated here.
!pip install scikit-posthocs #specify the growth of the 10 plants in each group group1 = [9, 10, 16, 9, 10, 5, 7, 13, 10, 9] group2 = [16, 19, 15, 17, 19, 11, 6, 17, 11, 9] group3 = [7, 9, 5, 8, 8, 14, 11, 9, 14, 8] data = [group1, group2, group3] #perform Dunn's test using a Bonferonni correction for the p-values import scikit_posthocs as sp sp.posthoc_dunn(data, p_adjust = 'bonferroni')
Output
The adjusted p-value for the distinction between groups 1 and 2 is 0.115458. The adjusted p-value for the distinction between groups 1 and 3 is 1.000000. The adjusted p-value for the distinction between groups 2 and 3 is 0.27465.
in conclusion
Dunn's test is widely used in many fields, including biology, psychology, and education, where it is necessary to compare the means of multiple samples to find whether there are significant differences between samples. It is particularly beneficial when the normality assumption is violated because it is a nonparametric test that does not rely on this assumption.
Dunn's test can be used in the field of education to compare the means of many sample data from different schools or classes to determine whether there are significant differences in the means of schools or classrooms. For example, you can use it to compare average test scores in different schools or average scores in different classrooms.
The above is the detailed content of How to perform Dunn's test in Python?. For more information, please follow other related articles on the PHP Chinese website!

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

How to teach computer novice programming basics within 10 hours? If you only have 10 hours to teach computer novice some programming knowledge, what would you choose to teach...

How to avoid being detected when using FiddlerEverywhere for man-in-the-middle readings When you use FiddlerEverywhere...


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 English version
Recommended: Win version, supports code prompts!

SublimeText3 Linux new version
SublimeText3 Linux latest version