to estimate the parameter values of a known distribution like normal distribution in this we estimate the parameters pop.mean and s.d
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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.
Parametric statistics is a branch of statistics that assumes data come from a type of probability distribution and makes inferences about the parameters of the distribution. See related link.
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!
Not necessarily.
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