Nonparametric Statistics: A Strange Name

Scott Pardo ()
Additional contact information
Scott Pardo: Ascensia Diabetes Care, Global Medical & Clinical Affairs

Chapter Chapter 14 in Statistical Analysis of Empirical Data, 2020, pp 181-195 from Springer

Abstract: Abstract Statistical methods all have assumptions about the data-generating process. Many of these assumptions concern the probability distribution of the population from which the sample was drawn. Typically the assumptions require a “parametric” form, namely that the population’s distribution relies on a small number of parameters, such as mean and standard deviation. Nonparametric methods attempt to provide a vehicle for inference that is free of such parametric assumptions.

Keywords: Parameters; Ranks; Rank transformation; Permutation tests (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-43328-4_14

Ordering information: This item can be ordered from
http://www.springer.com/9783030433284

DOI: 10.1007/978-3-030-43328-4_14

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().