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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

Q: What are the advantages and disadvantages of nonparametric statistics compared to the parametric statistics?

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Nonparametric tests are sometimes called distribution free statistics because they do not require that the data fit a normal distribution. Nonparametric tests require less restrictive assumptions about the data than parametric restrictions. We can perform the analysis of categorical and rank data using nonparametric tests.

David Sheskin has written: 'Handbook of parametric and nonparametric statistical procedures' -- subject(s): Mathematical statistics, Handbooks, manuals 'Handbook of parametric and nonparametric statistical procedures' -- subject(s): Mathematical statistics, Handbooks, manuals, etc, Handbooks, manuals

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 * * * * * 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!

There are both advantages and disadvantages of data collection methods in statistics. The main advantages are the metrics and correlation one can draw from statistics. The disadvantages stem from sampling errors.

Gregory W. Corder has written: 'Nonparametric statistics for non-statisticians' -- subject(s): Nonparametric statistics

There are many advantages and disadvantages of variance in statistics. One disadvantage is that you never know what answer you'll get.

One advantage of inferential statistics is that large predictions can be made from small data sets. However, if the sample is not representative of the population then the predictions will be incorrect.

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.

Yes, Chis squared test are among the most common nonparametric statistics tests.

Fred L. Ramsey has written: 'Birding Oregon' -- subject(s): Bird watching 'A small sample study of some non-parametric tests of location' -- subject(s): Nonparametric statistics, Statistical hypothesis testing

No. Descriptive statistics are those that characterise samples without attempting to draw conclusions. The purpose of them is to help investigators to form an understanding of what the data might be capable of telling them. Descriptive statistics include graphs as well as measures of location, scale, correlation, and so on. Parametric statistics are those that are based on probabilistic models (ie, mathematical models involving probability) that involve parameters. For instance, an investigator might assume that her results have come from a population that is normally distributed with a certain mean and standard deviation; this would be a parametric model. She could estimate this pair of parameters, the mean and standard deviation, using parametric statistics, or test hypotheses about them, again using parametric statistics. In either case the parametric statistics she uses would be based on the parametric mathematical model she has chosen for her data.