Parametric statistics is a branch of statistics which assumes that sample data comes from a population that can be adequately modeled by a probability distribution that has a fixed set of parameters.[1] Conversely a non-parametric model differs precisely in that it makes no assumptions about a parametric distribution when modeling the data.
Most well-known statistical methods are parametric.[2] Regarding nonparametric (and semiparametric) models, Sir David Cox has said, "These typically involve fewer assumptions of structure and distributional form but usually contain strong assumptions about independencies".[3]
The normal family of distributions all have the same general shape and are parameterized by mean and standard deviation. That means that if the mean and standard deviation are known and if the distribution is normal, the probability of any future observation lying in a given range is known.
Suppose that 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 observations from a normal distribution, then we predict there is a 1% chance that the 100th test score will be higher than 102.33 (that is, the mean plus 2.33 standard deviations), assuming that the 100th test score comes from the same distribution as the others. Parametric statistical methods are used to compute the 2.33 value above, given 99 independent observations from the same normal distribution.
Непараметрическая оценка одного и те же есть максимум первых 99 баллов. Нам не нужно делать никаких предположений о распределении результатов тестов, чтобы понять, что до того, как мы дали тест, с равной вероятностью наивысший балл был любым из первых 100. Таким образом, существует 1% -ная вероятность того, что 100-й балл будет выше, чем любой из 99, предшествовавших ему.
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