**Probability Sampling**

A sampling of probability implies that each element in the population has the same probability of doing well in the sample. One approach to accomplish random sampling would be to create a sampling frame initially and then utilize a generation of random number software program to extract a sample from the sampling frame (Zikmund, 2002). Random sampling has the strongest independence from bias, but can be the most costly sample for a particular level of sampling error in terms of effort and time (Brown, 1947).

**Non-probability Sampling**

A sampling of non-probability is often correlated with the development of case study analysis and qualitative research. For the above, case studies tend to be concentrated on small samples and are intended to evaluate a phenomenon in real life, not to create inferential statistics about the larger population (Yin, 2003).

**Simple Random Sampling**

A simple random sample implies that each case in the population has the same probability of being included in the sample. This method is easy to understand. There is an opportunity for each element of the population to choose a single random sampling method in the sample. (Alvi, 2016).

A simple random sample is obtained from a specific, random segment of the entire population to display the whole set of data, where each member has the same chance of being selected. There will be a sampling error with a simple random sample if the sample does not finish by accurately reflecting the population it is meant to represent.

Sampling elements that can participate in the survey should be available for the respondents and much effort is required in the simple random sampling method, particularly for the mass population that has the advantage of being unable to receive the statistical biases and the sample may be a fair representative of the population. In this study, sample random sampling approach is used to collect data from the respondents.