National EMSC Data Analysis Resource Center
What if your sample is not representative of your population?
Bias can be defined as:
Any systematic deviation from the truth that affects the conclusions you make based on your data.
If you don't design your project to identify and eliminate sources of bias, you may not be able to make the correct conclusions.
For example, imagine you are interested in assessing the attitudes and preferences of EMS providers in your state.
This type of bias is called selection bias because it resulted from the selection of a sample that was not representative of your population. To measure this, the EMS director requests that a group of his buddies complete the survey you've developed. Will the results reflect all of the providers in your state? Most likely not.
This type of bias is called selection bias because it resulted from the selection of a sample that was not representative of your population. We will discuss other sources of bias and how to overcome them shortly.