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In parametric statistics, the variable of interest is distributed according to some distribution that is determined by a small number of parameters. In non-parametric statistics there is no underlying parametric distribution.

In both cases, it is possible to look at measures of central tendency (mean, for example) and spread (variance) and, based on these, to carry out tests and make inferences.

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In parametric statistics, the variable of interest is distributed according to some distribution that is determined by a small number of parameters. In non-parametric statistics there is no underlying parametric distribution.

In both cases, it is possible to look at measures of central tendency (mean, for example) and spread (variance) and, based on these, to carry out tests and make inferences.

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1. A nonparametric statistic has no inference

2. A nonparametric statistic has no standard error

3. A nonparametric statistic is an element in a base population (universe of possibilities) where every possible event in the population is known and can be characterized

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That is utter rubbish and a totally irresponsible answer.

In parametric statistics, the variable of interest is distributed according to some distribution that is determined by a small number of parameters. In non-parametric statistics there is no underlying parametric distribution.

With non-parametric data you can compare between two (or more) possible distributions (goodness-of-fit), test for correlation between variables.

Some test, such as the Student's t, chi-square are applicable for parametric as well as non-parametric statistics.

I have, therefore, no idea where the previous answerer got his/her information from!

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Not necessarily.

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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.

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Non-Parametric statistics are statistics where it is not assumed that the population fits any parametrized distributions. Non-Parametric statistics are typically applied to populations that take on a ranked order (such as movie reviews receiving one to four stars). The branch of http://www.answers.com/topic/statistics known as non-parametric statistics is concerned with non-parametric http://www.answers.com/topic/statistical-model and non-parametric http://www.answers.com/topic/statistical-hypothesis-testing. Non-parametric models differ from http://www.answers.com/topic/parametric-statistics-1 models in that the model structure is not specified a priori but is instead determined from data. The term nonparametric is not meant to imply that such models completely lack parameters but that the number and nature of the parameters are flexible and not fixed in advance. Nonparametric models are therefore also called distribution free or parameter-free. * A http://www.answers.com/topic/histogram is a simple nonparametric estimate of a probability distribution * http://www.answers.com/topic/kernel-density-estimation provides better estimates of the density than histograms. * http://www.answers.com/topic/nonparametric-regression and http://www.answers.com/topic/semiparametric-regression methods have been developed based on http://www.answers.com/topic/kernel-statistics, http://www.answers.com/topic/spline-mathematics, and http://www.answers.com/topic/wavelet. Non-parametric (or distribution-free) inferential statistical methodsare mathematical procedures for statistical hypothesis testing which, unlike http://www.answers.com/topic/parametric-statistics-1, make no assumptions about the http://www.answers.com/topic/frequency-distribution of the variables being assessed. The most frequently used tests include

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