Question

Would you agree to the statement on random sampling methods. Please explain why.

In probability samples “each population element has a known (non-zero) chance of being chosen for the sample.” (StatTrek 2020). Some examples of probability samples are, simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling.

Simple random sampling is the population and sample consists of “N” objects, and an example is when people play the lottery. Stratified sampling is based on some type of characteristic, such as people being divided by a region, i.e. North, South, East, West, etc. The cluster sampling has the population is separated by a group and only one group, an example is conducting data on city taxes in Georgia. In Multistage sampling., a sample is conducted by different sample methods, in this method, part one may consist of stratified sampling, and part two consists of using the simple random sampling. Lastly, Systematic random sampling is creating a list of every population and taking a sample from one of the groups.

Answer 1

We would agree to the following statement on random sampling methods.

Systematic random sampling is creating a list of every population and taking a sample from one of the groups.

Explanation:

The statement about Systematic random sampling that it is creating a list of every population and taking a sample from one of the groups is incorrect. The correct definition of Systematic random sampling is as follows:

Systematic random sampling is creating a list of every population and taking a sample according to a random starting point but with a fixed periodic interval, obtained by dividing the population Size N by the desired sample size n.