Parameters are numerical values used to describe the overall characteristics, which can be obtained from a single measurement or inferred from a series of measurements of the population.
A statistic is a numerical value used to describe the characteristics of a sample, which can be obtained from a single measurement or inferred from a series of measurements taken on the sample.
There are similar forms of definitions for parameters and statistics, but there are also differences between the two.
In the measurement process of a group of samples, the statistic is fixed, but when we replace the sample and measure again, the value of the statistic may change. That is to say, the value of the statistic is indefinite and will change with the selection of the sample, while the parameter is a fixed value.
In research, we often use sample statistics to estimate population parameters, but as a subset of the population, samples cannot be completely equivalent to the population, and there may be some differences between sample statistics and population parameters.
Sampling error refers to the difference between sample statistics and the population parameters corresponding to the sample.
We cannot completely avoid the occurrence of sampling errors, as there are always differences between the sample and the population. Inferences based on limited information may not be accurate, and all we can do is use the correct methods to minimize sampling errors or keep them within the allowable range of the study.