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Is Paired samples T-test an example of nonparametric tests?
A paired samples t-test is an example of parametric (not nonparametric) tests.
What is a parametric test used to compare the means of two groups?
t-test
Is chi-square test parametric or non-parametric?
non-parametric I believe the above is a reductionistic assumption bassed upon ill-informed logic. Chi-square is a statistic that is related to the central limit theorem in the sense that proportions are in fact means, and that proportions are normally distributed (with a mean of pi [not 3.141592653...] and a variance of pi*(1-pi)). Therefore, we can perform a normal curve test for examining the difference between proportions such that Z squared = chi square on one degree of freedom. Since Z is indubitably a parametric test, and chi square can be related to Z, we can infer that it is, in fact, parametric. From another approach, a parametric test is a test that makes an assumption about the value of a parameter (the measure of the population rather than your sample) in a statistical density function. Since our expected frequencies are based upon either theory, or a mathematical assumption based upon the average of our presented frequencies, i.e. the mean, we are making an assumption about what the parameter of our distribution would be. Therefore, given this assumption, and the relationship of chi square to the normal curve, one can argue for chi square being a parametric test.
Distinguish between parameteric statistics and non - parameteric statistics?
The simplest answer is that parametric statistics are based on numerical data from which descriptive statistics can be calculated, while non-parametric statistics are based on categorical data. Takes two example questions: 1) Do men live longer than women, and 2), are men or women more likely to be statisticians. In the first example, you can calculate the average life span of both men and women and then compare the two averages. This is a parametric test. But in the second, you cannot calculate an average between "man" and "woman" or between "statistician" or "non-statistician." As there is no numerical data to work with, this would be a non-parametric test. The difference is vitally important. Because inferential statistics require numerical data, it is possible to estimate how accurate a parametric test on a sample is compared to the relevant population. However, it is not possible to make this estimation with non-parametric statistics. So while non-parametric tests are still used in many studies, they are often regarded as less conclusive than parametric statistics. However, the ability to generalize sample results to a population is based on more than just inferential statistics. With careful adherence to accepted random sampling, sample size, and data collection conventions, non-parametric results can still be generalizable. It is just that the accuracy of that generalization can not be statistically verified.
Definition of Parametric modeling?
it is the molding that is parametric
