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CHAPTER 14 – NONPARAMETRIC TESTS. Everything that we have done up until now in statistics has relied heavily on one major fact: that our data is normally distributed. We have been able to make inferences about population means (one-sample, two-sample z and t tests and analysis of variance), but in each case we
Module 9 Overview. ? Nonparametric Tests. ? Parametric vs. Nonparametric Tests. ? Restrictions of Nonparametric Tests. ? One-Sample Chi-Square Test. ? Chi-Square Test of Independence. ? Other Nonparametric Tests
A non-parametric statistical test is a test whose model does NOT specify conditions about the parameters of the population from which the sample was drawn. Most non-parametric tests apply to data in an ordinal scale, and some apply to data in nominal scale.
If the probability distributions of the statis- tical variables are unknown or are not as re- quired (e.g. normality assumption violated), then we may still apply nonparametric tests in order to test whether two or more groups follow the same distribution. Such methods do not assume the data to follow any prede- fined distribution
test hypotheses concerning population parameters, e.g., H or O. Thus, Ho: 11 = 12 is an appropriate hypothesis for a parametric test. Nonparametric tests are generally designed to test hypotheses that do not concern population parameters, but are based on the shape of the population frequency distributions.
Non parametric tests are used if the assumptions for the parametric tests are not met, and are commonly called distribution free tests. This is used when we want to compare two independent samples, and the assumptions underlying the t-test are not met.
Sum Test. 16.2 The Wilcoxon Signed. Rank Test. 16.3 The Kruskal-Wallis. Test. Nonparametric Tests. Introduction. The most commonly used statistical methods for inference ultimately rely on certain distributional assumptions. In regression, it is assumed that the residual variation is Normal. In testing of means, if data are
24 Jun 2010 Non-parametric tests. Outline. One Sample Test: Wilcoxon Signed-Rank. Two Sample Test: Wilcoxon–Mann–Whitney. Confidence Intervals. Summary. 2 / 30
26 Jul 2004 Nonparametric or distribution-free statistical methods. – Make very few assumptions about the form of the population distribution from which the data are sampled. – Based on ranks so they can be used on ordinal data. • Will concentrate on hypothesis tests but will also mention confidence interval
In statistical inference, or hypothesis testing, the traditional tests are called parametric tests because they depend on the specification of a probability distribution (such as the normal) except for a set of free parameters. Parametric tests are said to depend on distributional assumptions. Nonparametric tests, on the other hand,
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