STATISTICAL POWER OF HYPOTHESIS TESTING USING PARAMETRIC AND NONPARAMETRIC METHODS

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dc.contributor.authorAHMED, UMMI-KUSUM ISA
dc.date.accessioned2017-12-15T09:03:26Z
dc.date.available2017-12-15T09:03:26Z
dc.date.issued2015-04
dc.descriptionA Thesis Submitted to the Postgraduate School, Ahmadu Bello University, ZARIA, in Partial Fulfilment of the Requirement for the Award of Masters Degree in Statistics, in Department of Mathematics.en_US
dc.description.abstractParametric and nonparametric techniques are two broad statistical methods for significance testing among continuous random variables. In this thesis, parametric and nonparametric techniques were utilized to test the power of the tests. The real-life data is simulated, generated from normal and exponential distribution. Two nonparametric tests and their parametric tests equivalents were carried out, they include; Wilcoxon Rank-Sum test and Kruskal-Wallis test as well as their parametric counterparts; independent t-Test and One-Way Anova respectively. The comparison is based on the voilation of assumption of nomality and homogenity of variance. The tests were subjected to three cases depending on the sample sizes, n ≤ 30, and n ≥ 30 at α= 0.05, 0.01 and0.1 significance levels. It was observed from the analysis performed at n = 10 and n = 45 for Independentent T-test and Wilcoxon Rank-Sum test under the normal distribution that the power of the test are the same that is the two tests performed equally at all levels of significants, but at n=30 the two tests perfomed equally at α = 0.05 but at α= 0.01 and 0.1 the nonparametric is as powerful as the parametric. Under the exponential distribution, the parametric test is more powerful at α = 0.05 and 0.1 for n = 45 and 30, but the nonparametric is more powerful for n=10, at α = 0.01 the three size performed differently. Also under the normal distrbution for more than two independent samples, for the three sample sizes at α= 0.05 and 0.1 and also at α= 0.01 for n= 45 and 10, the Parametric test is more powerful but for n=30 the nonparametric test is as powerful as the Parametric Test. Under the exponential at the three levels for n= 45 and 30 the parametric test is more powerful but for n = 10 also at the three levels the nonparametric is more powerful. The power is also represented on bar chart. therefore the high chance of committing Type I orType II error is less when sample size is large and parametric test is more powerfulen_US
dc.identifier.urihttp://hdl.handle.net/123456789/9772
dc.language.isoenen_US
dc.subjectSTATISTICAL,en_US
dc.subjectPOWER,en_US
dc.subjectHYPOTHESIS,en_US
dc.subjectTESTING,en_US
dc.subjectPARAMETRIC,en_US
dc.subjectNONPARAMETRICen_US
dc.subjectMETHODSen_US
dc.titleSTATISTICAL POWER OF HYPOTHESIS TESTING USING PARAMETRIC AND NONPARAMETRIC METHODSen_US
dc.typeThesisen_US
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