Describe the differences between a probability sample and a nonprobability sample
The question which was presented in this week’s assignment is “what is the difference between a probability sample and a non-probability sample”. Before the differences between these samples can be understood each one has to be explored as its own entity. Below are the definitions and ideas of both probability and non-probability samples as well as showing what the differences are for each one.
Probability sample is defined as a completely random sample. This sampling method makes the different units being measured equal in the chance that they are going to be use. There is no set category or manipulation of the sample group. Here are two examples of a random sample. Playing the lottery – There is a completely random chance that you will get chosen. This is a great model for random sampling. Another is to put names in a hat and pull one out, again a simple random sample. Today, computers generate and pick names and numbers from a long list of database data. It is much easier to pull the samples and elect them to a research study than it was several years ago. There are many types of probability strategies in use today, they are Simple random sampling (Which is what has been described above), Systematic sampling which entails grouping population or members in similar ways, Stratified Random sampling – This sampling method uses different groups and randomly picks from those chosen groups. Finally there is Cluster Sampling – This type of sampling of units instead of individuals. (Strategies of Sampling Advantages and thier Disadvantages, 2014)
A Non-Probability sample is NOT random like its brother the probability sample. This is the main difference between the two sampling methods. The Non-Probability sample can be broken down into two types. The first being convenience sampling – convenience sampling is when the person being sampled is convenient to...