Parametric statistics is a branch of statistics that assumes that the data has come from a type of probability distribution and makes inferences about the parameters of the distribution. Most well-known elementary statistical methods are parametric.
Generally speaking parametric methods make more assumptions than non-parametric methods. 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 those 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 non-robustness, especially if care is taken to examine diagnostic statistics.
Other articles related to "parametric statistics, statistics, parametric":
... In statistics, the term non-parametric statistics has at least two different meanings The first meaning of non-parametric covers techniques that do not rely on data belonging to any ... As such it is the opposite of parametric statistics ... It includes non-parametric statistical models, inference and statistical tests ...
... 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..as the parametric case, and denote the opposite case, where the functional forms of the distributions are unknown, as the non-parametric case." ...
Famous quotes containing the word statistics:
“He uses statistics as a drunken man uses lamp-postsfor support rather than illumination.”
—Andrew Lang (18441912)