Explorable.com 40.7K reads

Share this page on your website:

Don't miss these related articles:

- 1Statistical Hypothesis Testing
- 2Relationships
- 3Correlation
- 4Regression
- 5Student’s T-Test
- 6ANOVA
- 7Nonparametric Statistics
- 8Other Ways to Analyse Data

There are several advantages of using nonparametric statistics. As can be expected, since there are fewer assumptions that are made about the sample being studied, nonparametric statistics are usually wider in scope as compared to parametric statistics that actually assume a distribution. This is mainly the case when we do not know a lot about the sample we are studying and making a priori assumptions about data distributions might not give us accurate results and interpretations. This directly translates into an increase in robustness.

However, there are also some disadvantages of nonparametric statistics. The main disadvantage is that the degree of confidence is usually lower for these types of studies. This means for the same sample under consideration, the results obtained from nonparametric statistics have a lower degree of confidence than if the results were obtained using parametric statistics. Of course, this is assuming that the study is such that it is valid to assume a distribution for the sample.

There are many experimental scenarios in which we can assume a normal distribution. For example if an experiment looks at the correlation between a healthy morning breakfast and IQ, the experimenter can assume beforehand that the IQs of the sample size follow a normal distribution within the sample, assuming the sample is chosen randomly from thepopulation. On the other hand, if this assumption is not made, then the experimenter is following nonparametric statistics methods.

However, there could be another experiment that measures the resistance of the human body to a strain of bacteria. In such a case, it is not possible to determine if the data will be normally distributed. It might happen that all people are resistant to the strain of bacteria under study or perhaps no one is. Again, there could be other considerations as well. It could be that people of a particular ethnicity are born with that resistance while none of the others are. In such cases, it is not right to assume a normal distribution of data. These are the situations in which nonparametric statistics should be used. There are many tests that tell us whether the data can be assumed to be normally distributed or not.

Full reference:

Explorable.com (Apr 26, 2010). Nonparametric Statistics. Retrieved Mar 17, 2018 from Explorable.com: https://explorable.com/nonparametric-statistics

The text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0).

This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give ** appropriate credit** and

That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution).

Search over 500 articles on psychology, science, and experiments.

Don't miss these related articles:

- 1Statistical Hypothesis Testing
- 2Relationships
- 3Correlation
- 4Regression
- 5Student’s T-Test
- 6ANOVA
- 7Nonparametric Statistics
- 8Other Ways to Analyse Data

Subscribe / Share

- Subscribe to our RSS Feed
- Like us on Facebook
- Follow us on Twitter
- Founder:
- Oskar Blakstad Blog
- Oskar Blakstad on Twitter

Explorable.com - 2008-2018

You are free to copy, share and adapt any text in the article, as long as you give *appropriate credit* and *provide a link/reference* to this page.