# Parametric test (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumptions they require and the data they use. Explain these differences.

### Essentials of Statistics for the B...

8th Edition
Frederick J Gravetter + 1 other
Publisher: Cengage Learning
ISBN: 9781133956570

### Essentials of Statistics for the B...

8th Edition
Frederick J Gravetter + 1 other
Publisher: Cengage Learning
ISBN: 9781133956570

#### Solutions

Chapter
Section
Chapter 15, Problem 1P
Textbook Problem

## Parametric test (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumptions they require and the data they use. Explain these differences.

Expert Solution
To determine

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

### Explanation of Solution

In a nonparametric test data is not required to fit a normal distribution, in-fact, it assumes that the data does not follow any specific distribution. Whereas in case of parametric test, the data follows a normal distribution.

The information about population parameter is not available in nonparametric test.

Non-parametric test uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. Parametric test uses data which is interval or ratio.

The nonparametric test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. Whereas, parametric test uses mean as a measure of central tendency.

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