1. Simple random sampling: Generally speaking, if a sample with n capacity is extracted from a population with n individuals, the probability of each individual being extracted is equal to n/n. Commonly used simple random sampling methods are: lottery method and random number method. Systematic sampling: when there are many individuals in the group, it is more troublesome to adopt simple random sampling.
2. At this time, the population can be divided into several balanced parts, and then an individual can be extracted from each part according to predetermined rules to get the required samples. Stratified sampling.
The benefits of random sampling are as follows:
1, improve the representativeness of the sample: random sampling can ensure that the probability of each individual being selected in the sample is equal, thus improving the representativeness of the sample. In this way, the results of the sample can reflect the characteristics of the population more accurately. Reduce prejudice: Random sampling can reduce artificial prejudice and tendency. If researchers choose samples selectively, the representativeness of the samples may be limited.
2, thus affecting the accuracy of the results. Random sampling can avoid this situation. Save time and resources: random sampling can effectively save time and resources. Because the probability of each individual being selected is known, researchers can choose the appropriate sample size according to their needs, thus avoiding blindly expanding the sample range.
3. Wide application: Random sampling is a general statistical method, which can be applied to various fields and research problems. Whether in social science, medicine, economics or other fields, random sampling can be used to improve the accuracy and representativeness of data. Strong operability: random sampling is a relatively simple and easy method.
4. Researchers can use simple tools such as lottery and random number table, which are reliable in most cases. Improving internal validity: random sampling is helpful to improve the internal validity of the study. Because the probability of each individual being selected is the same, the error caused by sample deviation can be reduced, thus improving the accuracy of research.