1. Random sampling method conforms to the principle of probability theory best in theory. Simple and easy, and the error calculation is convenient. This is a good sampling method when researchers don't know the proportion of all kinds of individuals in the studied population, or the differences between individuals in the population are small, or the number of samples is large.
2. The equidistant random sampling method is simpler than the simple random sampling method, and it can systematically sample the whole population. Therefore, on the whole, its sample is more accurate and the sampling error is smaller than the simple random sampling method. However, if the population fluctuates or changes periodically, the samples obtained by systematic sampling may have systematic deviation, which should be paid attention to when using equidistant random sampling method.
3. The advantages of stratified random sampling method are better representativeness and inference accuracy. It is suitable for the research object with a large number of whole units and large internal differences. When the number of samples is the same, its sampling error is smaller than that of simple random sampling method and equidistant random sampling method, while when the sampling error requirements are the same, its sample size is smaller than that of simple random sampling method and equidistant random sampling method. In addition, different sampling methods and proportions can be adopted for each layer according to specific conditions, which makes sampling more flexible.
4. The advantage of cluster random sampling method is that the samples are relatively concentrated, which is suitable for some specific research. For example, teaching experiments generally require research on a class basis and cannot disrupt the original teaching units. Therefore, this method is often used in educational scientific research, especially in educational experiments. In addition, in large-scale investigation and study, cluster random sampling is easy to organize and can save manpower, material resources and time; Its disadvantages are uneven sample distribution, poor representativeness and large sampling error (due to large differences between groups).