1. Simple random sampling numbers all the observation units in the survey population, and then randomly selects some observation units by lottery or random number table to form a sample. Advantages: simple operation, simple calculation of mean value, rate and corresponding standard error. Disadvantages: When the overall size is large, it is difficult to number them one by one. Second, systematic sampling is also called mechanical sampling and equidistant sampling, that is, the whole observation unit is divided into n parts according to a certain sequence number, and then K observation units are randomly selected from the first part, and then one observation unit is sequentially extracted from each part at equal intervals to form a sample. Advantages: easy to understand and easy to operate. Disadvantages: when the overall trend is periodic, it is easy to produce deviation. 3. The crowd was divided into groups by cluster sampling, and then several groups were randomly selected to form samples, and all groups were investigated. Advantages: easy to organize and save money. Disadvantages: sampling error is greater than simple random sampling. 4. layered sampling Firstly, the population is divided into several categories according to some characteristics that have great influence on the observation indicators, and then a certain number of observation units are randomly selected from each layer to form samples. There are two schemes: proportional distribution and optimal distribution. Advantages: good sample representativeness and reduced sampling error. The above four basic sampling methods all belong to single-stage sampling. In practical application, the whole sampling process is often divided into several stages according to the actual situation, which is called multi-stage sampling. The sampling errors of various sampling methods are generally: cluster sampling ≥ simple random sampling ≥ systematic sampling ≥ stratified sampling.
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