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# Kendall's W and Statistical hypothesis testing

## Difference between Kendall's W and Statistical hypothesis testing

### Kendall's W vs. Statistical hypothesis testing

Kendall's W (also known as Kendall's coefficient of concordance) is a non-parametric statistic. 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.

## Similarities between Kendall's W and Statistical hypothesis testing

Kendall's W and Statistical hypothesis testing have 2 things in common (in Unionpedia): Nonparametric statistics, Normal distribution.

### 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).

### Normal distribution

In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution.

### The list above answers the following questions

• What Kendall's W and Statistical hypothesis testing have in common
• What are the similarities between Kendall's W and Statistical hypothesis testing

## Kendall's W and Statistical hypothesis testing Comparison

Kendall's W has 12 relations, while Statistical hypothesis testing has 121. As they have in common 2, the Jaccard index is 1.50% = 2 / (12 + 121).

## References

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

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