Editing Statistical population (section)

==Mean==
The '''population mean''', or population [[expected value]], is a measure of the [[central tendency]] either of a [[probability distribution]] or of a [[random variable]] characterized by that distribution.<ref>{{cite book|last=Feller|first=William|title=Introduction to Probability Theory and its Applications, Vol I|year=1950|publisher=Wiley|isbn=0471257087|pages=221}}</ref> In a [[discrete probability distribution]] of a random variable <math>X</math>, the mean is equal to the sum over every possible value weighted by the probability of that value; that is, it is computed by taking the product of each possible value <math>x</math> of <math>X</math> and its probability <math>p(x)</math>, and then adding all these products together, giving <math>\mu = \sum x \cdot p(x)....</math>.<ref>Elementary Statistics by Robert R. Johnson and Patricia J. Kuby, [https://books.google.com/books?id=DWCAh7jWO98C&pg=PA279 p. 279]</ref><ref name=":1">{{Cite web|last=Weisstein|first=Eric W.|title=Population Mean|url=https://mathworld.wolfram.com/PopulationMean.html|access-date=2020-08-21|website=mathworld.wolfram.com|language=en}}</ref> An analogous formula applies to the case of a [[continuous probability distribution]]. Not every probability distribution has a defined mean (see the [[Cauchy distribution]] for an example). Moreover, the mean can be infinite for some distributions.

For a finite population, the population mean of a property is equal to the arithmetic mean of the given property, while considering every member of the population. For example, the population mean height is equal to the sum of the heights of every individual—divided by the total number of individuals. The ''[[sample mean]]'' may differ from the population mean, especially for small samples. The [[law of large numbers]] states that the larger the size of the sample, the more likely it is that the sample mean will be close to the population mean.<ref>Schaum's Outline of Theory and Problems of Probability by Seymour Lipschutz and Marc Lipson, [https://books.google.com/books?id=ZKdqlw2ZnAMC&pg=PA141 p. 141]</ref>