# Population Mean Value

A Population Mean Value is a population parameter that is a central tendency measure, the mean value, of a population.

**AKA:***µ, µ*_{x}**Context:**- It equals a specific value under the null hypothesis.
- It can be estimated by using the sample mean value.

**Example(s):**- the population mean person age for all humans alive at this moment.

**Counter-Example(s):****See:**Population Mean Standard Deviation.

## References

### 2017

- (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Mean
- For a data set, the terms arithmetic mean, mathematical expectation, and sometimes average are used synonymously to refer to a central value of a discrete set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers
*x*_{1},*x*_{2}, ...,*x*is typically denoted by [math]\bar{x}[/math], pronounced "_{n}*x*bar". If the data set were based on a series of observations obtained by sampling from a statistical population, the arithmetic mean is termed the**sample mean**(denoted [math]\bar{x}[/math]) to distinguish it from the**population mean**(denoted**[math]\mu[/math]**or**[math]\mu_x[/math]**).^{[1]}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 dictates that the larger the size of the sample, the more likely it is that the sample mean will be close to the population mean.^{[2]}

- For a data set, the terms arithmetic mean, mathematical expectation, and sometimes average are used synonymously to refer to a central value of a discrete set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers

### 1996

- (Underbill & Bradfield, 1996) ⇒ Les Underhill, and Dave Bradfield (1996). Introstat. Juta and Company Ltd, page 181 ISBN:07021388X
- We now have three concepts, each called a
**mean**: the mean of a sample (chapter 1), the mean of a probability distribution (chapter 5) and now the mean of a population. The sample mean is used to estimate the population mean. When a probability distribution is chosen as a**statistical model**for a population, one of the criteria for determining the parameters of the probability distribution is that the mean of the probability distribution should be equal to the population mean. This paragraph so far is also true when we replace the word**mean**with the word**variance**. It is a universal convention to use the symbols [math]\mu[/math] and [math]\sigma^2[/math] for the population mean and population variance, respectively, and the fact that these symbols are also used for the mean and the variance of a probability distribution causes no confusion. Because these are important notions, we risk saying them again. The population mean and variance are quantities that belong to the population as a whole. If you could examine the entire population of interest then you could determine the**one true value**for the population mean and the**one true value**for the population variance. Usually, it is impracticable to do a census of every member of a population to determine the population mean. The standard procedure is to take a random sample from the population of interest and**estimate**[math]\mu[/math], the population mean, by means of [math]\bar{x}[/math], the sample mean.

- We now have three concepts, each called a