Parametric statistics is a branch of statistics which assumes that the data have come from a type of probability distribution and makes inferences about the parameters of the distribution.^{[1]} Most wellknown elementary statistical methods are parametric.^{[2]} The difference between parametric model and nonparametric model is that the former has a fixed number of parameters, while the latter grows the number of parameters with the amount of training data.^{[3]}
Generally speaking parametric methods make more assumptions than nonparametric methods.^{[4]} If those extra assumptions are correct, parametric methods can produce more accurate and precise estimates. They are said to have more statistical power. However, if assumptions are incorrect, parametric methods can be very misleading. For that reason they are often not considered robust. On the other hand, parametric formulae are often simpler to write down and faster to compute. In some, but definitely not all cases, their simplicity makes up for their nonrobustness, especially if care is taken to examine diagnostic statistics.^{[5]}
Contents

Example 1

History 2

See also 3

References 4
Example
Suppose we have a sample of 99 test scores with a mean of 100 and a standard deviation of 1. If we assume all 99 test scores are random samples from a normal distribution we predict there is a 1% chance that the 100th test score will be higher than 102.365 (that is the mean plus 2.365 standard deviations) assuming that the 100th test score comes from the same distribution as the others. The normal family of distributions all have the same shape and are parameterized by mean and standard deviation. That means if you know the mean and standard deviation, and that the distribution is normal, you know the probability of any future observation. Parametric statistical methods are used to compute the 2.365 value above, given 99 independent observations from the same normal distribution.
A nonparametric estimate of the same thing is the maximum of the first 99 scores. We don't need to assume anything about the distribution of test scores to reason that before we gave the test it was equally likely that the highest score would be any of the first 100. Thus there is a 1% chance that the 100th is higher than any of the 99 that preceded it.
History
Statistician Jacob Wolfowitz coined the statistical term "parametric" in order to define its opposite in 1942:
"Most of these developments have this feature in common, that the distribution functions of the various stochastic variables which enter into their problems are assumed to be of known functional form, and the theories of estimation and of testing hypotheses are theories of estimation of and of testing hypotheses about, one or more parameters. . ., the knowledge of which would completely determine the various distribution functions involved. We shall refer to this situation. . .as the parametric case, and denote the opposite case, where the functional forms of the distributions are unknown, as the nonparametric case."^{[6]}
See also
References

^ Geisser, S.; Johnson, W.M. (2006) Modes of Parametric Statistical Inference, John Wiley & Sons, ISBN 9780471667261

^ Cox, D.R. (2006) Principles of Statistical Inference, Cambridge University Press, ISBN 9780521685672

^ Murphy, Kevin (2012). Machine Learning: A Probabilistic Perspective. MIT. p. 16.

^ Corder; Foreman (2009) Nonparametric Statistics for NonStatisticians: A StepbyStep Approach, John Wiley & Sons, ISBN 9780470454619

^ Freedman, D. (2000) Statistical Models: Theory and Practice, Cambridge University Press, ISBN 9780521671057

^ Wolfowitz, J. (1942) Annals of Mathematical Statistics, XIII, p. 264 (1942)
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