BTM8107-8 Non-Parametric Tests

Name: BTM8107-8 Non-Parametric Tests
Brand: Northcentral University
Price: 2 USD
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Author: Benjamin Griffin

Examination of non-parametric testing methods in statistical research.

Benjamin Griffin

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BTM8107-8-7 1
NORTHCENTRAL UNIVERSITY
ASSIGNMENT COVER SHEET
Based on the statistical analyses provided in the assignment, explain when it is more appropriate
to use non-parametric tests over parametric tests. Specifically, compare and contrast the
Wilcoxon Signed Rank test and Mann-Whitney U test in terms of their application to different
types of data. Discuss the results and interpretations of the tests performed on the given datasets
(Activity 4A.sav and Activity 4B.sav). Additionally, explain the concept of statistical power in
the context of non-parametric tests and how it affects the ability to detect differences in data.
Word count requirement: 800-1000 words.

BTM8107-8-7 2
Student:
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BTM8107-8
Statistics II
Faculty Use Only

BTM8107-8-7 3
Non-Parametric Tests
BTM8107-8
Introduction
Parametric tests address estimations of population parameters (mean) while non-parametric tests
are distribution free methods relying on the ranking of observations, making fewer assumptions
than other tests, however, they do make distributional assumptions but just not normality (Field,
2013).
Part A
1. Common reasons to select non-parametric tests over the parametric alternatives:
 Sample size: Nonparametric tests should be used with small sample sizes and when a
researcher cannot rely on the central limit theorem (Field, 2013).
 Distribution of variables: If data is normally distributed then parametric tests are used,
otherwise non parametric test are used.
 Scale of data: If data is categorical or ordinal, non-parametric tests are used.
 Non-parametric tests are preferred when Median is used to summarize the data consisting
of outliers.
2. Statistical Powers
Non-parametric tests are ideal when the sample size is small, however, they are less powerful
than parametric tests because they result in a loss of information about the magnitude of
differences between scores (Field, 2013). If certain data meets appropriate assumptions,
parametric tests have greater power to detect the effects than non-parametric tests.

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Northcentral University

Subject

Statistics

BTM8107-8 Non-Parametric Tests

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