- Average
- a number that describes the central tendency of the data; there are a number of specialized averages, including the arithmetic mean, weighted mean, median, mode, and geometric mean.

- Central Limit Theorem
- Given a random variable with known mean
*μ*and known standard deviation,*σ*, we are sampling with size*n*, and we are interested in two new RVs: the sample mean, $\stackrel{\u2013}{X}$. If the size (*n*) of the sample is sufficiently large, then $\stackrel{\u2013}{X}$ ~*N*(*μ*, $\frac{\sigma}{\sqrt{n}}$). If the size (*n*) of the sample is sufficiently large, then the distribution of the sample means will approximate a normal distributions regardless of the shape of the population. The mean of the sample means will equal the population mean. The standard deviation of the distribution of the sample means, $\frac{\sigma}{\sqrt{n}}$, is called the standard error of the mean.

- Finite Population Correction Factor
- adjusts the variance of the sampling distribution if the population is known and more than 5% of the population is being sampled.

- Mean
- a number that measures the central tendency; a common name for mean is "average." The term "mean" is a shortened form of "arithmetic mean." By definition, the mean for a sample (denoted by $\stackrel{\u2013}{x}$) is $\stackrel{\u2013}{x}\text{=}\frac{\text{Sumofallvaluesinthesample}}{\text{Numberofvaluesinthesample}}$, and the mean for a population (denoted by
*μ*) is $\mu \text{=}\frac{\text{Sumofallvaluesinthepopulation}}{\text{Numberofvaluesinthepopulation}}$.

- Normal Distribution
- a continuous random variable with pdf $f(x)\text{=}\frac{1}{\sigma \sqrt{2\pi}}{e}^{\frac{\u2013{\text{(}x\text{}\u2013\text{}\mu )}^{2}}{2{\sigma}^{2}}}$, where
*μ*is the mean of the distribution and*σ*is the standard deviation.; notation:*X*~*N*(*μ*,*σ*). If*μ*= 0 and*σ*= 1, the random variable, Z, is called the**standard normal distribution**.

- Sampling Distribution
- Given simple random samples of size
*n*from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution.

- Standard Error of the Mean
- the standard deviation of the distribution of the sample means, or $\frac{\sigma}{\sqrt{n}}$.

- Standard Error of the Proportion
- the standard deviation of the sampling distribution of proportions