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therefore the high chance of committing Type I orType II error is less when sample size is large and parametric test is more powerful.Edustore.NG is an academic website built in Nigeria that is registered with the All these tests are based on the assumption of normality i.e., the source of data is considered to be normally distributed. The other type ,hypothesis testing ,is discussed in this chapter. 65 .

And so here, you're pretty comfortable to say that the running time actually is Gaussian distribution. At first glance this may not appear important. The comparison is based on the voilation of assumption of nomality and homogenity of variance.

Lecture 7: Parametric Hypothesis Testing Course Home Syllabus Lecture Slides Lecture Videos ... And you just try to superimpose the curve with this one, which is a Gaussian PDF with mean 103.5 and variants 373. Download this complete Project material titled; Statistical Power Of Hypothesis Testing Using Parametric And Nonparametric Methods with abstract, chapters 1-5, references, and questionnaire. 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. In this thesis, parametric and nonparametric techniques were utilized to test the power of the tests. If you need a primer on the basics, read my hypothesis testing overview. Importance of Parametric test in Research Methodology.

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. And you see that they actually look very much alike. For example estimating a population's mean from a sample mean. Nonparametric tests are a shadow world of parametric tests. Recent molecular studies have incorporated the parametric bootstrap method to test a priori hypotheses when the results of molecular based phylogenies are in conflict with these hypotheses. The process of testing research hypothesis is important for researchers, academicians, statisticians, policy implementers among other users. Find materials for this course in the pages linked along the left. Objectives In covering these objectives the following terms will be introduced: We have shown previously that statistical inference enables general conclusions to be drawn from specific data. 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 enables concerned individuals to deduce meaning as well as make decisions based on the outcomes of the Hypothesis Testing One type of statistical inference, estimation, was discussed in Chapter 5.

Text Book : Basic Concepts and Methodology for the Health Sciences 3 The important parametric tests are: z-test; t-test; χ 2-test, and; F-test. 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.
July 2016 ... Chapter - 4 Formulating and Testing Hypothesis Page. Your use of the MIT OpenCourseWare site and materials is subject to our 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. Flash and JavaScript are required for this feature.This is one of over 2,200 courses on OCW. With more than 2,400 courses available, OCW is delivering on the promise of open sharing of knowledge. Parametric and nonparametric techniques are two broad statistical methods for significance testing among continuous random variables. The real-life data is simulated, generated from normal and exponential distribution. Related Pairs of Parametric and Nonparametric Tests. MIT OpenCourseWare makes the materials used in the teaching of almost all of MIT's subjects available on the Web, free of charge.

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