Since the range only takes into account two values from the entire dataset, it may be heavily influenced by outliers in the data. Note that the interquartile range and semi-interquartile are similar in concept to the median. Note that if you add all the deviation scores for a dataset together, you automatically get the mean for that dataset. Hence the formula is modified for samples compared with whole populations. Since the standard deviation is calculated in a similar fashion to the mean, i.e. take every data point in the population or sample into consideration, it is not of great value for highly skewed datasets.
Any statistic can have a standard error.
The standard error of a statistic is the standard deviation of the sampling distribution of that statistic. The standard deviation is an index of how closely individual data points cluster around the mean - SD refers to individual data points. One way to determine this is to repeat the experiment many times and to determine the mean of the means. However, this is tedious and frequently impossible. Fortunately, standard errors can be calculated from a single experiment.