Sampling is generally the process of extracting the number of subsets from the observation

Sampling is generally the process of extracting the number of subsets from the observation, in order to find out the characteristics. In addition, random sampling is the sample, which has chosen in the random manner and known as randomly chosen sample. Usually, random sampling avoids all the bias and unwanted effects from the observation.
(Dura, Driga, &Nita, 2010) states that simple random sampling is the sampling that requires every subunit belonging to the general observation and have equal chances of getting selected in the sample. However, simple random sampling is often difficult to employ in numerous surveys and experiments.
As every sample has equal probability of being selecting in simple random sampling so the sample size in this sampling method must be greater than few hundred. Then, the simple random sampling is always applying in the appropriate manner. This sampling method is theoretically easy to understand for the people or researcher but proves more difficult when practically implemented. Furthermore, simple random sampling is the method that helps the researcher to save time and money because getting data from a sample is more advisable and practical.
On the other hand, stratified random sampling is the process that divides the population into smaller groups where population is the total set of observation. The stratified random sampling method generally used to highlight a specific subgroup within the population because this method ensures the presence of the key subgroups in the sample.
Using the stratified random sampling researchers have greater statistical precision as compared to simple random sampling because the variability within the subgroups is lower as compare to the variation in simple random sampling when dealing with the entire population.
Advantages of simple random sampling method is that it is the fairer method as well as, help to reduce any type of bias involved if applied correctly than the stratified random sampling. Simple random sampling involves the random selection of data from the whole population, which results that each sample is equally likely to occur. But, on the other hand the stratified random sampling divide the whole population into the smaller groups.
In the simple random sampling method, it is not necessary that the researcher have prior knowledge of the data collected and it is the basic method of collecting data because for this no technical knowledge is required. However, in stratified sampling researchers are investigating the entire population before applying the sampling method.
Disadvantages of the simple random sampling method are that, large sample size sometimes appears to be problematic because every member of the population has equal chance of being selecting. Therefore, large population is difficult to manage. On the other hand, in the stratified method, the researcher should identify the every member of a population and classify every member into only one subpopulation.
Generally, quality of the data used in the simple random sampling mostly depends on researcher’s perspective because quality of the data is better when collected by the experienced researcher but the data may not be good if the researcher is less experienced. However, the main disadvantage of the stratified random sampling is overlapping because when the random sampling has performed, the members in the multiple subgroups are more likely to chosen.
Solanki ; Singh states that if the population is heterogeneous and consideration of cost limits the sample size then with the help of simple random sampling it may be difficult to achieve a sufficiently precise estimate from the whole population (2015). Then in such a situation, simple random sampling is the one that most widely used technique in the survey sampling providing representative sample from diverse range of cross sections of the population.
Generally, the stratified random sampling is better to use than the simple random sampling because the accuracy of statistical result is higher in the stratified sampling than simple sampling. Due to the high accuracy involved in the stratified random sampling, it is probable that the required sample size will be much lesser and help the researcher in saving time and efforts. Moreover, according to Buddhakulsomsiri ; Parthanadee (2007), in the most health care systems the main problem encountered is the estimation of accuracy in the processing health care bills submitted to third party payer and the problem would be solved with the use of stratified in the sampling method. Mostly the simple random sampling id best when the whole population is available and on the other hand, the stratified random sampling is best when there are specific sub- groups are available to investigate.