**DEFINITIONS**

**Standard Deviation: **

**Standard deviation** measures the scatter in a group of observations. It is a calculated summary of the distance each observation in a data set is from the __mean__. **Standard deviation** gives us a good idea whether a set of observations are loosely or tightly clustered around the __mean__.

**Sampling Error: **

**Sampling error **is the difference between a population characteristic and the __estimate__ of this characteristic based on a __sample__. Sampling error arises because it's often not possible to collect data on a whole __population__, and __samples__ aren't often identical in character to their parent __population__. The larger a __sample __becomes the more likely it will look like the whole __population__ it was sampled from, resulting in a smaller sampling error. If we did a complete enumeration of the __population__, such as in a census, there would be no sampling error.

**Standard Error: **

The **Standard Error** (SE) is one way of measuring the sampling error of an __estimate__. The theory shows that there are about two chances in three that an __estimate__ from a __sample __is within one standard error of the true value (the value for the whole __population__). As such, the larger the standard error, the less confident we are that the __estimate__ from the __sample__ is close to the true value.

There are several types of Standard Error (SE).** **A commonly used type of standard error in the Australian Bureau of Statistics is the Standard Error of the Mean.

**Relative Standard Error:**

The relative standard error (RSE) is the standard error of the estimate divided by the __estimate__ itself. It is another way of expressing the standard error to make interpretation easier. It's useful for comparing the size of the standard error across different samples, and is often expressed as a percentage. As with the standard error, the higher the RSE, the less confident we are that the __estimate__ from the __sample__ is close to the true value.