Answer:
The standard error of the distribution of sample means estimates how an individual sample mean is near or far from the actual population mean of this distribution.
Explanation:
A sampling distribution is a 'special' distribution created from samples of the same size from a population.
A sampling distribution of the mean is created taking many random samples of the same size, and, for each sample, we calculate its mean. This distribution has its own mean, called expected value and its own standard deviation, called standard error.
Well, any sample has a variation, an average difference between this individual sample mean and the expected value (or the mean for a sampling distribution). This is, in fact, what the standard error estimates.
As a result, a greater standard error for a random sample indicates that this sample is more different from the expected value (the sample mean does not represent the population mean) than one which has less difference from that expected value.