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# Friedman test and Kendall's W

## Difference between Friedman test and Kendall's W

### Friedman test vs. Kendall's W

The Friedman test is a non-parametric statistical test developed by Milton Friedman. Kendall's W (also known as Kendall's coefficient of concordance) is a non-parametric statistic.

## Similarities between Friedman test and Kendall's W

Friedman test and Kendall's W have 3 things in common (in Unionpedia): Nonparametric statistics, Probability distribution, Statistical hypothesis testing.

### Nonparametric statistics

Nonparametric statistics is the branch of statistics that is not based solely on parameterized families of probability distributions (common examples of parameters are the mean and variance).

### Probability distribution

In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.

### Statistical hypothesis testing

A statistical hypothesis, sometimes called confirmatory data analysis, is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables.

### The list above answers the following questions

• What Friedman test and Kendall's W have in common
• What are the similarities between Friedman test and Kendall's W

## Friedman test and Kendall's W Comparison

Friedman test has 24 relations, while Kendall's W has 12. As they have in common 3, the Jaccard index is 8.33% = 3 / (24 + 12).

## References

This article shows the relationship between Friedman test and Kendall's W. To access each article from which the information was extracted, please visit:

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