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Probability sampling is one of the tools of statistics. It helps researchers identify the representatives among the large population segments. The researcher may not be able to include the whole population segment in the study. Therefore, it is essential to identify the representatives among them. The researcher in the study must avoid biasness in the selection process. The selection should remain fair to conduct a good study. There are two forms of sampling that are:
- Probability Sampling technique helps the researcher to form samples using random sampling.
- Non-Probability Sampling technique is a non-random sampling process. It includes convenience sampling or sampling based on the researcher’s judgement.
Probability Sampling
The random sampling technique is used in probability sampling to select representatives. Non-experienced researchers can better use the probability sampling technique. Therefore, fresh researchers must not confuse themselves in probability sampling.
The researcher can use digital technology to perform random sampling processes in the modern age. The purpose of probability sampling in statistics assignment is to select the representatives among the targeted sample size of the study.
The Methods of Probability Sampling
The advantages and disadvantages of the probability sampling method and its usage are as below.
Simple Random Sampling
One of the easiest probability sampling is the Simple Random Sampling technique in statistics. In a simple random sampling technique, the researcher must maintain a list of the population segment used in the study. The list is designed so that every participant has an equal chance of being selected as a representative. The researcher needs to select the study representatives from that list randomly. The process of simple random sampling technique is a time taking process. This sampling process is considered an authentic sampling process. It s a recommended sampling method for the students of statistics.
Stratified Random Sampling
Stratified random sampling may refer to a proportional random sampling technique. In statistics, the stratified sampling technique categorises the data for population sampling. The population segment is divided into different categories. These categories may include age, sex, region, education, or other factors. It will be helpful for the statistics student to use this probability sampling method in their assignments. The sampling process must ensure that the categorise must not overlap. Then, the researcher will randomly select the representatives from each category in an equal ratio.
This type of statistical sampling is most suitable for studying different population segments. The results reflect a clear understanding of each category of the study. It is a time-consuming process to complete the process of stratified random sampling. The researcher at the first stage needs to define categories. Then the researcher will categorise the population samples. Finally, a random sampling process will run to get representatives from the population.
Systematic Random Sampling
In systematic random sampling, the researcher sets some fixed intervals or values. The representatives from the population size are selected based on the fixed intervals or values. This random sampling technique resembles the basic arithmetic operation. The difference between the two numerical values remains the same. This method will help statistics students to deal huge sample size.
For instance, if a researcher is examing 100 patients in a clinic. The first step is to set some fixed intervals. The interval must be less than 100 and cannot be in a negative value. For this case study, assume to pick number 10. In the next step, we need to choose the individuals. This study assumes that the researcher selects eight individuals in the selected subject. Therefore the study will select the representatives. The representative patients in this study will be 10, 18, 26, 34, 42, 50, 58, 66, 74, 82, 90 and 98.
The systematic random sampling technique helps the select researcher representatives from the population segment. This process does not need a random sampling run.
Cluster (Area) Random Sampling
It is a statistical sampling technique used to handle a large population. We can not use the random sampling technique for large population segments. The initial stage of random cluster sampling defines a certain area or boundaries for the sample selection. The statistic student may limit the huge sample by filtering to include schools, cities, towns and other microfilters. Then the researcher must apply a random sampling technique to ensure that every population segment in the boundary has equal chances. This technique is easy to use. However, if the selected representatives are opposite in nature, then the research will not be able to draw a solid conclusion. In this case, only buying assignment online can save you.
Multi-Stage Sampling
It comprises an integration of two or more probability sampling methods. Moreover, this technique splits the sample size into small groups. Then the researcher will select two or more methods of probability sampling. Each of the probability sampling techniques will be run separately. The results gathered from the first ran will be taken to the second filter. The process will continue to complete all the filters. Eventually, the researcher will get the smallest representative group from the population segment. The advanced level of research helps ensure a refined selection of the representatives among the population size. Hence, it will help generate useful results.
Conclusion
We can use any probability sampling technique in statistics assignments. A nonprobability method is difficult for statistics assignments as it uses a subjective method. The researcher selects the population segment among the total population in probability sampling. The researcher’s bias will be controlled, and the study will achieve authentic results.
