The Kruskal–Wallis test by ranks, Kruskal–Wallis H test (named after William Kruskal and W. Allen Wallis), or one-way ANOVA on ranks is a non-parametric method for testing whether samples originate from the same distribution. It is used for **comparing two or more independent samples of equal or different sample sizes**.

- 1 When would you use a Kruskal-Wallis test?
- 2 What is the difference between ANOVA and Kruskal-Wallis when to use each?
- 3 What is the difference between Kruskal-Wallis test and Mann Whitney test?
- 4 Why might we use the Kruskal-Wallis test instead of ANOVA?
- 5 What is Kruskal-Wallis test with example?
- 6 How do you use a Kruskal-Wallis table?
- 7 What does Kruskal-Wallis test compare?
- 8 Is Kruskal-Wallis test the same as ANOVA?
- 9 What are the assumptions of Kruskal-Wallis test?
- 10 How does the Kruskal-Wallis test work?

## When would you use a Kruskal-Wallis test?

Typically, a Kruskal-Wallis H test is used **when you have three or more categorical, independent groups**, but it can be used for just two groups (i.e., a Mann-Whitney U test is more commonly used for two groups).

## What is the difference between ANOVA and Kruskal-Wallis when to use each?

The other assumption of one-way anova is that the variation within the groups is equal (homoscedasticity). While **Kruskal-Wallis does not assume that the data are normal, it does assume that the different groups have the same distribution, and groups with different standard deviations have different distributions**.

## What is the difference between Kruskal-Wallis test and Mann Whitney test?

The major difference between the Mann-Whitney U and the Kruskal-Wallis H is simply that **the latter can accommodate more than two groups**. Both tests require independent (between-subjects) designs and use summed rank scores to determine the results.

## Why might we use the Kruskal-Wallis test instead of ANOVA?

The Kruskal-Wallis test is a nonparametric (distribution free) test, and is used **when the assumptions of one-way ANOVA are not met**. Both the Kruskal-Wallis test and one-way ANOVA assess for significant differences on a continuous dependent variable by a categorical independent variable (with two or more groups).

## What is Kruskal-Wallis test with example?

A Kruskal-Wallis test is **used to determine whether or not there is a statistically significant difference between the medians of three or more independent groups**.

Example of a Kruskal-Wallis Test.

Drug 1 | Drug 2 | Drug 3 |
---|---|---|

70 | 45 | 62 |

61 | 66 | 44 |

50 | 47 | 48 |

44 | 42 | 77 |

## How do you use a Kruskal-Wallis table?

Quote from video:

*Test the kruskal-wallis test is a version of the independent measures or one way ANOVA that can be performed on ordinal or ranked. Data ordinal data is displayed in the table below.*

## What does Kruskal-Wallis test compare?

The Kruskal–Wallis test (1952) is a nonparametric approach to the one-way ANOVA. The procedure is used to compare **three or more groups on a dependent variable that is measured on at least an ordinal level**.

## Is Kruskal-Wallis test the same as ANOVA?

It is used for comparing two or more independent samples of equal or different sample sizes. It extends the Mann–Whitney U test, which is used for comparing only two groups. **The parametric equivalent of the Kruskal–Wallis test is the one-way analysis of variance (ANOVA)**.

## What are the assumptions of Kruskal-Wallis test?

The assumptions of the Kruskal-Wallis test are similar to those for the Wilcoxon-Mann-Whitney test. **Samples are random samples, or allocation to treatment group is random.** **The two samples are mutually independent.** **The measurement scale is at least ordinal, and the variable is continuous**.

## How does the Kruskal-Wallis test work?

The test **determines whether the medians of two or more groups are different**. Like most statistical tests, you calculate a test statistic and compare it to a distribution cut-off point. The test statistic used in this test is called the H statistic.