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|Table of Contents Univariate Data Sampling Distributions Sampling Distributions|
|See also: chi-square distribution, F distribution, t distribution|
Suppose we know the average weight and its standard deviation of 27 year old men in Switzerland (for simplicity let's assume that the average weight is 72.2 kg, and the standard deviation is 5.4 kg). If we now randomly select 40 men and determine their weights, we will obtain a specific mean which is close to the average weight of the whole population. Repeating the selection will result in a somewhat different mean. If this selection were repeated several times, the resulting histogram of the calculated means would be approximately normal even if the probability distribution of the whole population (in a statistical sense) of all 27 year old men in Switzerland is not normal.
The nature of the distribution of a sample statistic may be determined either mathematically, or at least empirically, by simulating sampling experiments on a computer. Since the properties of a sample statistic depend on its distribution, sampling distributions are used to compare among statistics and to infer knowledge about some test statistic.
There are a few special sampling distributions which are often needed
in statistical calculations and statistical tests:
Last Update: 2006-Jšn-18