The difference between standard error and standard deviation

The standard deviation is the square root of the average of the squared deviations of all the data points of a variable from its mean; it is an indicator used to measure the degree of dispersion. No need for further explanation. The standard error is a special type of standard deviation; it refers to the case where samples are drawn from a population, and assuming that the sample size remains constant in such sampling, an infinite number of samples can be taken theoretically. For a certain statistic calculated for each of these samples – such as the average value of each sample – a new distribution can also be formed. This new distribution is obtained by sampling from the original population distribution, and it is a theoretical distribution (in practice, it is impossible to draw such an infinite number of samples); it is known as a sampling distribution. Then the standard deviation of this new sampling distribution is called the standard error.