Statistics - (Non) Parametrics (method|statistics)

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Parametric statistics is a branch of statistics.

A Parametric method, such as t-tests is a method that is based on fixed assumption that the sample data comes from a population that follows a probability distribution (normal, poisson, ) based on a fixed set of parameters.

Ie: the assumed distribution is fix. For instance, for the normal distribution, the parameters <math>\mu</math> and <math>\sigma</math> are fixed in its density

Conversely a non-parametric model differs precisely in that the parameter set (or feature set in machine learning) is not fixed and can increase, or even decrease if new relevant information is collected.

IT systems collect

Parametric methods are inadequate for the analysis of IT collected data since they rely on strong assumptions on the distribution of data (i.e. normality) that are not met by operations data.

This lack of relevance of classical, parametric statistics can be explained by history. The origins of statistics reach back to the 17th century, when computation was expensive and data was a sparse resource, leading mathematicians to spend a lot of effort to avoid calculations.

Documentation / Reference

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