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BTM8107-8 Non-Parametric Tests

Examination of non-parametric testing methods in statistical research.

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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.

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    BTM8107 - 8 - 7 2 Student : THIS FORM MUST BE COMPLETELY FILLED IN Follow these p rocedures: If requested by your instructor, please include an assignment cover sheet. This will become the first page of your assignment. In addition, your assignment header should include your last name, first initial, course code, dash, and assignment number. This should be left justified, with the page number right justified. For example: DoeJXXX0000 - 1 1 Save a copy of your assignments: You may need to re - submit an assignment at your instructor’s request. Make sure you save your files in accessible location. Academic integrity: All work submitted in each course must be your own original work. This includes all assignments, exams, term papers, and other projects required by your instructor. BTM8107 - 8 Statistics II Faculty Use Only

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    BTM8107 - 8 - 7 3 Non - Parametric Tests BTM8107 - 8 Introduction Parametric tests address estimation s of population parameters (mean) while non - parametric tests are distribution free methods rely ing on the ranking of observations, making fewer assumptions than other tests, however, they do make distributional ass umptions but just not normality (Field, 2013). P art A 1. Common reasons to select non - parametric tests over the parametric alternatives:  Sample size: Nonparametric tests should be used with small sample size s 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.  N on - parametric test s are preferred w hen Median is used to summarize the data consist ing of outliers. 2. Statistical P owers 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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