Series of Posts

This series of post is called inferential statistics and is meant as a brief introduction to the next series. The next series of posts is on confidence intervals. After the confidence intervals series we have a series on hypothesis testing.

In statistics, the standard deviation of a sample statistic is called the standard error. The standard error of the mean measures variability among all your sample means. You have taken several samples from the same population and have computed the mean for each sample. Do not confuse this with the standard deviation which refers to the variability of the individual values themselves.

The standard error shows variability. The formula is sigma over the square root of the sample size. Sigma is the population standard deviation.

Sample Mean

The sample mean, x bar, is an estimate of the population mean, mu.

Population Mean

Often, this is not known. The mean of the population. Mean is a point estimate.

Sample Variance

The sample variance S squared is an estimate of the population variance: sigma squared.

Population Variance

sigma squared

Population Standard Deviation

The symbol for population standard deviation is σ (sigma).