Sampling techniques can be divided into two categories: probability and non-probability. In probability sampling, each population member has a known, non-zero chance of participating in the study. Randomization or chance is the core of probability sampling technique.

In non-probability sampling, on the other hand, sample group members are selected non-randomly; therefore, in non-probability sampling only certain members of the population has a chance to participate in the study.

Probability sampling comprises the following sampling techniques:

• Simple random sampling
• Stratified random sampling
• Systematic random sampling
• Multistage random sampling
• Cluster sampling

Application of Probability Sampling: an Example

Let’s suppose, your dissertation topic is ‘A study into employee motivation of ABC Company and the ways of increasing it’. You chose survey primary data collection method to achieve research objectives.

Probability sampling process comprises four stages:

1. Identifying an appropriate sampling frame based on your research question(s) and objectives. ABC Company has 400 employees and accordingly, your sampling frame would be 400.

2. Determining a suitable sample size. You may decide that the sample size of 60 employees should be sufficient for the purposes of this research.

3. Choosing the most appropriate sampling technique and selecting the samples. In this case, simple random sampling, the most basic form of probability sampling technique can be applied through using a table of randomly generated numbers.  Websites such asGenerate Data, Graph Pad,Mockaroo and many others can be used to do this task easily and quickly.

Now, all you have to do is to choose a starting point in the table (a row and column number) and look at the random numbers that appear there. In this case, since the data run into three digits, the random numbers would need to contain three digits as well. You need to ignore all the random numbers after 400, since your target population has only 400 members. Also, choose a specific number only once and if a number recurs, simply skip it and move to the next number. In this way, the first 60 different numbers between 001 and 400 that represent 60 employees of ABC Company constitute your sample group.

4. Checking if the sample is representative of the population.

Advantages of Probability Sampling

1. The absence of systematic error and sampling bias
2. Higher level of reliability of research findings
3. Increased accuracy of sampling error estimation
4. The possibility to make inferences about the population

Disadvantages of Probability Sampling

1. Higher complexity compared to non-probability sampling
2. More time consuming
3. Usually more expensive than non-probability sampling